The project aims to improve object detection in optical remote sensing images using an adaptive Mask RCNN model. This technology is critical for applications in both civilian and military domains, where fast and accurate identification of objects in satellite imagery is required.
Surveillance and Security:
Urban Planning and Infrastructure:
Environmental Monitoring:
Surveillance and Security:
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
import os
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import cv2
def load_images_from_folder(folder):
images = []
filenames = []
original_sizes = []
for filename in os.listdir(folder):
img = cv2.imread(os.path.join(folder, filename))
if img is not None:
original_size = img.shape[:2] # Capture original size
original_sizes.append(original_size)
images.append(img)
filenames.append(filename)
return images, filenames, original_sizes
def load_annotations_from_folder(folder, filenames, target_size=(224, 224)):
annotations = []
for idx, filename in enumerate(filenames):
annot_filename = os.path.splitext(filename)[0] + '.txt'
annot_file = os.path.join(folder, annot_filename)
boxes = []
with open(annot_file, 'r') as file:
for line in file.readlines():
parts = line.replace('(', '').replace(')', '').replace(',', ' ').split()
i = 0
while i < len(parts) - 4:
try:
x_min = int(float(parts[i].strip()))
y_min = int(float(parts[i+1].strip()))
x_max = int(float(parts[i+2].strip()))
y_max = int(float(parts[i+3].strip()))
class_id = int(parts[i+4].strip())
boxes.append([x_min, y_min, x_max, y_max, class_id])
i += 10
except ValueError as e:
print(f"Error parsing line '{line}': {e}")
i += 10
annotations.append(boxes)
return annotations
Overview:
The NWPU VHR-10 dataset comprises images extracted from Google Earth, one of the most widely used platforms for high-resolution satellite imagery. The dataset was manually annotated by experts to ensure accuracy.
Content:
Classes: The dataset contains 10 classes of objects, which include both man-made and natural objects commonly found in remote sensing imagery. The classes are:
Airplane
Ship
Storage Tank
Baseball Diamond
Tennis Court
Basketball Court
Ground Track Field
Harbor
Bridge
Vehicle
Total Images: The NWPU VHR-10 dataset contains a total of 800 images.
Annotations: The dataset includes annotated bounding boxes for each of the 10 object classes. These annotations identify the specific regions within each image where the respective objects are located.
## Suppress warnings for a cleaner output
import warnings
warnings.filterwarnings('ignore')
# Core libraries for data processing and mathematical operations
import matplotlib.pyplot as plt
import numpy as np
# Deep learning libraries
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.keras import layers
import json
from PIL import Image, ImageDraw
import skimage.draw
from mrcnn.visualize import display_instances, display_top_masks
from mrcnn.utils import extract_bboxes
from mrcnn.utils import Dataset
from matplotlib import pyplot as plt
from mrcnn.config import Config
from mrcnn import utils
WARNING:tensorflow:From C:\Users\Mega-PC\anaconda3\lib\site-packages\tf_keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.
import os
# Walk through NWPU directory and list number of files
for dirpath, dirnames, filenames in os.walk(r".\NWPU VHR-10 dataset"):
print(f"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.")
There are 3 directories and 1 images in '.\NWPU VHR-10 dataset'. There are 0 directories and 650 images in '.\NWPU VHR-10 dataset\ground truth'. There are 0 directories and 150 images in '.\NWPU VHR-10 dataset\negative image set'. There are 0 directories and 650 images in '.\NWPU VHR-10 dataset\positive image set'.
positive_image_folder = r".\NWPU VHR-10 dataset\positive image set"
ground_truth_folder = r".\NWPU VHR-10 dataset\ground truth"
# Define output annotation file
output_annotation_file = "annotations.json"
# Define the mapping of class names to IDs
class_mapping = {
"airplane": 1,
"ship": 2,
"storage tank": 3,
"baseball diamond": 4,
"tennis court": 5,
"basketball court": 6,
"ground track field": 7,
"harbor": 8,
"bridge": 9,
"vehicle": 10
}
image_id = 0
coco_data = {"images": [], "annotations": [], "categories": []}
for filename in os.listdir(ground_truth_folder):
if filename.endswith('.txt'):
txt_file_path = os.path.join(ground_truth_folder, filename)
image_file_path = os.path.join(positive_image_folder, filename.replace('.txt', '.jpg'))
with open(txt_file_path, 'r') as f:
lines = f.readlines()
# Open the image to get its height and width
with Image.open(image_file_path) as img:
image_width, image_height = img.size
# Add image entry to coco_data
image_entry = {
"id": image_id,
"file_name": image_file_path,
"height": image_height,
"width": image_width
}
coco_data['images'].append(image_entry)
image_id += 1
# Iterate over each line in the text file
for line in lines:
line = line.strip() # Remove leading/trailing white spaces
# Extract the bounding box coordinates and object class
values = line.split('),')
# Ensure the line contains at least 2 values (x1,y1),(x2,y2),a
if len(values) >= 3:
# Extract the values for bounding box coordinates and object class
x1, y1 = map(int, values[0].replace('(', '').split(','))
x2, y2 = map(int, values[1].replace('(', '').split(','))
obj_class = int(values[2])
# Create a dictionary for the annotation
annotation = {
'image_id': image_id - 1, # Image ID corresponds to index in the images list
'category_id': obj_class, # Assuming object class is the category ID
'bbox': [x1,y1,x2,y2], # COCO bbox format: [x, y, width, height]
'area': (x2 - x1) * (y2 - y1), # Assuming area is bbox width * height
'iscrowd': 0 # Set to 0 for non-crowd annotations
}
# Append the annotation to the coco_data
coco_data['annotations'].append(annotation)
# Define categories
categories = [{"id": class_id, "name": class_name} for class_name, class_id in class_mapping.items()]
# Add categories to coco_data
coco_data['categories'] = categories
# Write the coco_data to the output annotation file
with open(output_annotation_file, 'w') as f:
json.dump(coco_data, f, indent=4)
class CocoLikeDataset(utils.Dataset):
def load_data(self,annotation_json,images_dir):
json_file=open(annotation_json)
coco_json=json.load(json_file)
json_file.close()
source_name="coco_like"
for category in coco_json['categories']:
class_id=category['id']
class_name=category['name']
if class_id<1:
print('Error: Class id for "{}" cannot be less than one '.format(class_name))
return
self.add_class(source_name,class_id,class_name)
annotations={}
for annotation in coco_json['annotations']:
image_id = annotation['image_id']
if image_id not in annotations:
annotations[image_id]=[]
annotations[image_id].append(annotation)
seen_images={}
for image in coco_json['images']:
image_id=image['id']
if image_id in seen_images:
print("Warning: Skipping duplicate image id : {}".format(image))
else:
seen_images[image_id]=image
try:
image_file_name=image['file_name']
image_width = image['width']
image_height = image['height']
except KeyError as key:
print("Warning: Skipping image (id: {}) with missing key : {}".format(image_id,key))
image_path=os.path.abspath(os.path.join(images_dir,image_file_name))
image_annotations=annotations[image_id]
self.add_image(
source=source_name,
image_id=image_id,
path=image_path,
width=image_width,
height=image_height,
annotations=image_annotations
)
def load_mask(self,image_id):
image_info = self.image_info[image_id]
annotations = image_info['annotations']
instance_masks = []
class_ids = []
for annotation in annotations:
class_id = annotation['category_id']
mask = Image.new('1', (image_info['width'], image_info['height']))
mask_draw = ImageDraw.ImageDraw(mask, '1')
print(annotation['bbox'])
array=annotation['bbox']
mask_draw.rectangle(array, fill=1)
bool_array = np.array(mask) > 0
instance_masks.append(bool_array)
class_ids=np.append(class_ids,class_id)
mask = np.dstack(instance_masks)
class_ids = np.array(class_ids, dtype=np.int32)
return mask, class_ids
## Suppress warnings for a cleaner output
import warnings
warnings.filterwarnings('ignore')
# Core libraries for data processing and mathematical operations
import matplotlib.pyplot as plt
import numpy as np
# Deep learning libraries
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.keras import layers
import json
from PIL import Image, ImageDraw
import skimage.draw
from mrcnn.visualize import display_instances, display_top_masks
from mrcnn.utils import extract_bboxes
from mrcnn.utils import Dataset
from matplotlib import pyplot as plt
from mrcnn.config import Config
from mrcnn import utils
WARNING:tensorflow:From C:\Users\Mega-PC\anaconda3\lib\site-packages\tf_keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.
import os
# Walk through NWPU directory and list number of files
for dirpath, dirnames, filenames in os.walk(r".\NWPU VHR-10 dataset"):
print(f"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.")
There are 3 directories and 1 images in '.\NWPU VHR-10 dataset'. There are 0 directories and 650 images in '.\NWPU VHR-10 dataset\ground truth'. There are 0 directories and 150 images in '.\NWPU VHR-10 dataset\negative image set'. There are 0 directories and 650 images in '.\NWPU VHR-10 dataset\positive image set'.
positive_image_folder = r".\NWPU VHR-10 dataset\positive image set"
ground_truth_folder = r".\NWPU VHR-10 dataset\ground truth"
# Define output annotation file
output_annotation_file = "annotations.json"
# Define the mapping of class names to IDs
class_mapping = {
"airplane": 1,
"ship": 2,
"storage tank": 3,
"baseball diamond": 4,
"tennis court": 5,
"basketball court": 6,
"ground track field": 7,
"harbor": 8,
"bridge": 9,
"vehicle": 10
}
image_id = 0
coco_data = {"images": [], "annotations": [], "categories": []}
for filename in os.listdir(ground_truth_folder):
if filename.endswith('.txt'):
txt_file_path = os.path.join(ground_truth_folder, filename)
image_file_path = os.path.join(positive_image_folder, filename.replace('.txt', '.jpg'))
with open(txt_file_path, 'r') as f:
lines = f.readlines()
# Open the image to get its height and width
with Image.open(image_file_path) as img:
image_width, image_height = img.size
# Add image entry to coco_data
image_entry = {
"id": image_id,
"file_name": image_file_path,
"height": image_height,
"width": image_width
}
coco_data['images'].append(image_entry)
image_id += 1
# Iterate over each line in the text file
for line in lines:
line = line.strip() # Remove leading/trailing white spaces
# Extract the bounding box coordinates and object class
values = line.split('),')
# Ensure the line contains at least 2 values (x1,y1),(x2,y2),a
if len(values) >= 3:
# Extract the values for bounding box coordinates and object class
x1, y1 = map(int, values[0].replace('(', '').split(','))
x2, y2 = map(int, values[1].replace('(', '').split(','))
obj_class = int(values[2])
# Create a dictionary for the annotation
annotation = {
'image_id': image_id - 1, # Image ID corresponds to index in the images list
'category_id': obj_class, # Assuming object class is the category ID
'bbox': [x1,y1,x2,y2], # COCO bbox format: [x, y, width, height]
'area': (x2 - x1) * (y2 - y1), # Assuming area is bbox width * height
'iscrowd': 0 # Set to 0 for non-crowd annotations
}
# Append the annotation to the coco_data
coco_data['annotations'].append(annotation)
# Define categories
categories = [{"id": class_id, "name": class_name} for class_name, class_id in class_mapping.items()]
# Add categories to coco_data
coco_data['categories'] = categories
# Write the coco_data to the output annotation file
with open(output_annotation_file, 'w') as f:
json.dump(coco_data, f, indent=4)
class CocoLikeDataset(utils.Dataset):
def load_data(self,annotation_json,images_dir):
json_file=open(annotation_json)
coco_json=json.load(json_file)
json_file.close()
source_name="coco_like"
for category in coco_json['categories']:
class_id=category['id']
class_name=category['name']
if class_id<1:
print('Error: Class id for "{}" cannot be less than one '.format(class_name))
return
self.add_class(source_name,class_id,class_name)
annotations={}
for annotation in coco_json['annotations']:
image_id = annotation['image_id']
if image_id not in annotations:
annotations[image_id]=[]
annotations[image_id].append(annotation)
seen_images={}
for image in coco_json['images']:
image_id=image['id']
if image_id in seen_images:
print("Warning: Skipping duplicate image id : {}".format(image))
else:
seen_images[image_id]=image
try:
image_file_name=image['file_name']
image_width = image['width']
image_height = image['height']
except KeyError as key:
print("Warning: Skipping image (id: {}) with missing key : {}".format(image_id,key))
image_path=os.path.abspath(os.path.join(images_dir,image_file_name))
image_annotations=annotations[image_id]
self.add_image(
source=source_name,
image_id=image_id,
path=image_path,
width=image_width,
height=image_height,
annotations=image_annotations
)
def load_mask(self,image_id):
image_info = self.image_info[image_id]
annotations = image_info['annotations']
instance_masks = []
class_ids = []
for annotation in annotations:
class_id = annotation['category_id']
mask = Image.new('1', (image_info['width'], image_info['height']))
mask_draw = ImageDraw.ImageDraw(mask, '1')
print(annotation['bbox'])
array=annotation['bbox']
mask_draw.rectangle(array, fill=1)
bool_array = np.array(mask) > 0
instance_masks.append(bool_array)
class_ids=np.append(class_ids,class_id)
mask = np.dstack(instance_masks)
class_ids = np.array(class_ids, dtype=np.int32)
return mask, class_ids
LOADING A COCOJSON DATA
image_id = 1
# load the image
image = dataset_train.load_image(image_id)
# load the masks and the class ids
mask, class_ids = dataset_train.load_mask(image_id)
# display_instances(image, r1['rois'], r1['masks'], r1['class_ids'],
# dataset.class_names, r1['scores'], ax=ax, title="Predictions1")
# extract bounding boxes from the masks
bbox = extract_bboxes(mask)
# display image with masks and bounding boxes
display_instances(image, bbox, mask, class_ids, dataset_train.class_names)
[575, 114, 635, 162] [72, 305, 133, 369] [210, 317, 273, 384] [306, 374, 344, 420] [447, 531, 535, 632] [546, 605, 625, 707] [632, 680, 720, 790]
import cv2
def resize_images(images, target_size=(1024, 1024)):
resized_images = []
for img in images:
resized_img = cv2.resize(img, target_size)
resized_images.append(resized_img)
return resized_images
def resize_annotations(annotations, original_sizes, target_size=(1024, 1024)):
resized_annotations = []
for idx, annot in enumerate(annotations):
original_height, original_width = original_sizes[idx]
scale_x = target_size[0] / original_width
scale_y = target_size[1] / original_height
resized_boxes = []
for box in annot:
x_min = int(box[0] * scale_x)
y_min = int(box[1] * scale_y)
x_max = int(box[2] * scale_x)
y_max = int(box[3] * scale_y)
class_id = box[4]
resized_boxes.append([x_min, y_min, x_max, y_max, class_id])
resized_annotations.append(resized_boxes)
return resized_annotations
# Resizing positive images
target_size = (1024, 1024) # Define your target size here
resized_positive_images = resize_images(positive_images, target_size)
resized_negative_images = resize_images(negative_images, target_size)
# Resizing positive annotations
resized_positive_annotations = resize_annotations(positive_annotations, positive_original_sizes, target_size)
Multiple Individual Augmentations:
Pros: This method allows the model to learn from a wider variety of simple changes, potentially improving its ability to generalize from each type of transformation individually. It increases the effective size of the training dataset more significantly, which can be beneficial for training deep learning models.
Cons: The main drawback is increased computational and storage requirements since you're generating multiple images for each original image in the dataset. It might also introduce redundancy if the transformations are too mild or too correlated. Best Practices
Balanced Approach: Often, a mix of both strategies is employed. For example, you might apply mild transformations (like slight rotations and flips) individually to generate multiple images and then perform a few combined transformations (like moderate zoom followed by a slight rotation) to create more diverse scenarios. Experimentation and Validation: It's important to experiment with different strategies and validate their impact on model performance. Monitoring how each type of augmentation affects overfitting, underfitting, and validation accuracy can guide you to optimize the augmentation pipeline. Resource Management: Consider your computational resources and training time. More images mean longer training times and more disk space. If resources are limited, focusing on the most impactful transformations might be necessary.
import numpy as np
import cv2
import matplotlib.pyplot as plt
def adjust_brightness_negative(image, brightness_factor):
""" Adjust the brightness of an image. """
return cv2.convertScaleAbs(image, alpha=brightness_factor, beta=0)
def flip_horizontal_negative(image):
"""Flip image horizontally."""
return cv2.flip(image, 1) # 1 means horizontal flip
def flip_vertical_negative(image):
"""Flip image vertically."""
return cv2.flip(image, 0) # 0 means vertical flip
def generate_augmented_images_negative(original_img, zoom_factors, angles, brightness_factors):
augmented_images = []
# 1. Zoom + Rotation:
zoom_factor = np.random.choice(zoom_factors)
new_width = int(original_img.shape[1] * zoom_factor)
new_height = int(original_img.shape[0] * zoom_factor)
zoomed_rotation_img = cv2.resize(original_img, (new_width, new_height))
start_x = (new_width - original_img.shape[1]) // 2
start_y = (new_height - original_img.shape[0]) // 2
zoomed_rotation_img = zoomed_rotation_img[start_y:start_y + original_img.shape[0], start_x:start_x + original_img.shape[1]]
angle = np.random.choice(angles)
zoomed_rotation_img = cv2.warpAffine(zoomed_rotation_img, cv2.getRotationMatrix2D((zoomed_rotation_img.shape[1] / 2, zoomed_rotation_img.shape[0] / 2), angle, 1), (zoomed_rotation_img.shape[1], zoomed_rotation_img.shape[0]))
augmented_images.append(zoomed_rotation_img)
# 2. Brightness Adjustment + Vertical Flip:
v_flip_img = flip_vertical_negative(original_img)
brightness_factor = np.random.choice(brightness_factors)
v_flip_img = adjust_brightness_negative(v_flip_img, brightness_factor)
augmented_images.append(v_flip_img)
# 3. Zoom + Brightness Adjustment:
zoom_factor = np.random.choice(zoom_factors)
new_width = int(original_img.shape[1] * zoom_factor)
new_height = int(original_img.shape[0] * zoom_factor)
zoomed_brightness_img = cv2.resize(original_img, (new_width, new_height))
start_x = (new_width - original_img.shape[1]) // 2
start_y = (new_height - original_img.shape[0]) // 2
zoomed_brightness_img = zoomed_brightness_img[start_y:start_y + original_img.shape[0], start_x:start_x + original_img.shape[1]]
brightness_factor = np.random.choice(brightness_factors)
zoomed_brightness_img = adjust_brightness_negative(zoomed_brightness_img, brightness_factor)
augmented_images.append(zoomed_brightness_img)
# Rotation augmentation
angle = np.random.choice(angles)
rotated_img = cv2.warpAffine(original_img, cv2.getRotationMatrix2D((original_img.shape[1] / 2, original_img.shape[0] / 2), angle, 1), (original_img.shape[1], original_img.shape[0]))
augmented_images.append(rotated_img)
# Horizontal Flip
h_flip_img = flip_horizontal_negative(original_img)
augmented_images.append(h_flip_img)
return augmented_images
def display_augmentations_negative(original_img):
zoom_factors = [1.2, 1.4, 1.6, 1.8, 2.0] # Factors for zooming
angles = [10, 20, 30, -10, -20, -30] # Degrees for rotation
brightness_factors = [0.5, 0.7, 1.3, 1.5] # Factors for brightness adjustment
aug_imgs = generate_augmented_images_negative(original_img, zoom_factors, angles, brightness_factors)
fig, axes = plt.subplots(1, len(aug_imgs), figsize=(20, 5)) # Adding original image as well
axes[0].imshow(cv2.cvtColor(original_img, cv2.COLOR_BGR2RGB))
axes[0].set_title("Original")
axes[0].axis('off')
for ax, img in zip(axes[1:], aug_imgs):
ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
ax.axis('off')
plt.tight_layout()
plt.show()
# Example usage (assuming you have resized_negative_images defined)
for img in resized_negative_images[:1]: # Displaying first 5 images for example
display_augmentations_negative(img)
import numpy as np
import matplotlib.pyplot as plt
import cv2 # Ensure OpenCV is imported for image processing
# Initialize lists to hold all negative images
all_negative_images = []
# Iterate over each negative image
for img in resized_negative_images:
zoom_factors = [1.2, 1.4, 1.6, 1.8, 2.0] # Factors for zooming
angles = [10, 20, 30, -10, -20, -30] # Degrees for rotation
brightness_factors = [0.5, 0.7, 1.3, 1.5] # Factors for brightness adjustment
# Generate augmented images
augmented_images = generate_augmented_images_negative(img, zoom_factors, angles, brightness_factors)
# Append the original image first
all_negative_images.append(img)
# Extend list with the augmented images
all_negative_images.extend(augmented_images)
# Displaying the original and augmented images
fig, axes = plt.subplots(2, 4, figsize=(20, 8)) # Adjust the subplot grid as necessary
for ax, img in zip(axes.flatten(), all_negative_images[:8]): # Ensure we don't exceed the grid size
ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) # Convert BGR to RGB
ax.axis('off')
plt.tight_layout()
plt.show()
# Calculate the number of images in all_images
number_of_negative_images = len(all_negative_images)
# Print the result
print("Total number of images Negative images after data augmentation:", number_of_negative_images)
# Download TorchVision repo to use some files from
# references/detection
!pip install pycocotools --quiet
!git clone https://github.com/pytorch/vision.git
!git checkout v0.3.0
!cp vision/references/detection/utils.py ./
!cp vision/references/detection/transforms.py ./
!cp vision/references/detection/coco_eval.py ./
!cp vision/references/detection/engine.py ./
!cp vision/references/detection/coco_utils.py ./
Cloning into 'vision'... remote: Enumerating objects: 502456, done. remote: Counting objects: 100% (16066/16066), done. remote: Compressing objects: 100% (809/809), done. remote: Total 502456 (delta 15275), reused 15996 (delta 15230), pack-reused 486390 Receiving objects: 100% (502456/502456), 973.34 MiB | 23.03 MiB/s, done. Resolving deltas: 100% (468510/468510), done. fatal: not a git repository (or any of the parent directories): .git
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from PIL import Image
import numpy as np
# Basic python and ML Libraries
import os
import random
import numpy as np
import pandas as pd
# for ignoring warnings
import warnings
warnings.filterwarnings('ignore')
# We will be reading images using OpenCV
import cv2
# xml library for parsing xml files
from xml.etree import ElementTree as et
# matplotlib for visualization
import matplotlib.pyplot as plt
import matplotlib.patches as patches
# torchvision libraries
import torch
import torchvision
from torchvision import transforms as torchtrans
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
# these are the helper libraries imported.
from engine import train_one_epoch, evaluate
import utils
import transforms as T
# for image augmentations
import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2
from torch.cuda.amp import GradScaler, autocast
scaler = GradScaler()
from torchvision.transforms import functional as F
class ImagesDataset(torch.utils.data.Dataset):
def __init__(self, files_dir, width, height,ID,CLASS,BOX, transforms=None):
self.transforms = transforms
self.files_dir = files_dir
self.bbox_dir= bbox_dir
self.height = height
self.width = width
# sorting the images for consistency
# To get images, the extension of the filename is checked to be jpg
self.imgs = ID
self.box=BOX
self.Class=CLASS
# classes: 0 index is reserved for background
self.classes= [_,1,2,3,4,5,6,7,8,9,10]
def __getitem__(self, idx):
img_name = self.imgs[idx]
image_path = os.path.join(self.files_dir, img_name +'.jpg')
# reading the images and converting them to correct size and color
img = cv2.imread(image_path)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32)
img_res = cv2.resize(img_rgb, (self.width, self.height), cv2.INTER_AREA)
#diving by 255
img_res /= 255.0
# annotation file
annot_filename = img_name + '.txt'
annot_file_path = os.path.join(self.bbox_dir, annot_filename)
bb=[]
# cv2 image gives size as height x width
wt = img.shape[1]
ht = img.shape[0]
# convert boxes into a torch.Tensor
bbox=self.box[idx]
for box in bbox:
xmin_corr = (box[0]/wt)*self.width
xmax_corr = (box[2]/wt)*self.width
ymin_corr = (box[1]/ht)*self.height
ymax_corr = (box[3]/ht)*self.height
bb.append([xmin_corr, ymin_corr, xmax_corr, ymax_corr])
boxes = torch.as_tensor(bb, dtype=torch.float32)
# boxes = torch.as_tensor([xmin_corr, ymin_corr, xmax_corr, ymax_corr], dtype=torch.float32)
# getting the areas of the boxes
area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
#area = (boxes[3] - boxes[1]) * (boxes[2] - boxes[0])
# suppose all instances are not crowd
iscrowd = torch.zeros((boxes.shape[0],), dtype=torch.int64)
labels = torch.as_tensor(self.Class[idx], dtype=torch.int64)
target = {}
target["boxes"] = boxes
target["labels"] = labels
target["area"] = area
target["iscrowd"] = iscrowd
# image_id
image_id=idx
# image_id = torch.tensor([idx])
target["image_id"] = image_id
if self.transforms:
# print(img_res.shape)
# print(target['boxes'])
# print(labels.view(-1))
sample = self.transforms(image = img_res,
bboxes = target['boxes'],
labels = labels.view(-1))
img_res = sample['image']
# img_res = img_res.permute(1, 2, 0)
target['boxes'] = torch.Tensor(sample['bboxes'])
target["labels"] = labels.view(-1)
# target['boxes'] = sample['bboxes']
# print("---------------------------")
# print(img_res.shape)
# print(target['boxes'])
# print(labels.view(-1))
# print("----------------------------")
# print(target)
return torch.tensor(img_res), target
def __len__(self):
return len(self.imgs)
# Send train=True fro training transforms and False for val/test transforms
def get_transform(train):
if train:
return A.Compose([
A.HorizontalFlip(0.5),
# ToTensorV2 converts image to pytorch tensor without div by 255
ToTensorV2(p=1.0)
], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})
else:
return A.Compose([
ToTensorV2(p=1.0)
], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})
# defining the files directory and testing directory
files_dir = '/content/drive/MyDrive/dataset/positive image set'
neg_dir = '/content/drive/MyDrive/dataset/negative image set'
bbox_dir = '/content/drive/MyDrive/dataset/ground truth'
#random_dir ='/kaggle/input/random-test'
def getressources():
# Chemin vers votre répertoire contenant les fichiers
repertoire = "/content/drive/MyDrive/dataset/ground truth"
# Initialisation des listes
noms_fichiers = []
box_data = []
class_data = []
# Parcours des fichiers dans le répertoire
for nom_fichier in os.listdir(repertoire):
chemin_fichier = os.path.join(repertoire, nom_fichier)
# Vérification que le chemin correspond à un fichier et non à un répertoire
if os.path.isfile(chemin_fichier):
nom_sans_extension = os.path.splitext(nom_fichier)[0]
with open(chemin_fichier, 'r') as f:
contenu_fichier = f.read()
noms_fichiers.append(nom_sans_extension)
lignes = contenu_fichier.strip().split('\n')
boxes = []
classes = []
for ligne in lignes:
elements = ligne.split(',')
box = [int(elem.strip("() ")) for elem in elements[:4]]
classe = int(elements[4])
boxes.append(box)
classes.append(classe)
box_data.append(boxes)
class_data.append(classes)
return(noms_fichiers,box_data,class_data)
ID,BOX,CLASS=getressources()
print(len(ID))
650
# Transformation for image resizing and normalization
transform = transforms.Compose([
transforms.Resize((1024, 1024)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# use our dataset and defined transformations
dataset = ImagesDataset(files_dir, 480, 480,ID,CLASS,BOX,transforms= get_transform(train=True))
dataset_test = ImagesDataset(files_dir, 480, 480,ID,CLASS,BOX,transforms= get_transform(train=False))
# split the dataset in train and test set
torch.manual_seed(1)
indices = torch.randperm(len(dataset)).tolist()
# train test split
test_split = 0.2
tsize = int(len(dataset)*test_split)
dataset = torch.utils.data.Subset(dataset, indices[:-tsize])
dataset_test = torch.utils.data.Subset(dataset_test, indices[-tsize:])
# define training and validation data loaders
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=5, shuffle=True, num_workers=4,
collate_fn=utils.collate_fn)
data_loader_test = torch.utils.data.DataLoader(
dataset_test, batch_size=5, shuffle=False, num_workers=4,
collate_fn=utils.collate_fn)
# Model setup
def get_object_detection_model(num_classes):
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
return model
# to train on gpu if selected.
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
num_classes = 11
# get the model using our helper function
model = get_object_detection_model(num_classes)
# move model to the right device
model.to(device)
# construct an optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.005,
momentum=0.9, weight_decay=0.0005)
# and a learning rate scheduler which decreases the learning rate by
# 10x every 3 epochs
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=3,
gamma=0.1)
Downloading: "https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth" to /root/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth 100%|██████████| 160M/160M [00:01<00:00, 85.6MB/s]
device
device(type='cuda')
# training for 8 epochs # sgd
num_epochs = 15
for epoch in range(num_epochs):
# training for one epoch
train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
# update the learning rate
lr_scheduler.step()
# evaluate on the test dataset
evaluate(model, data_loader_test, device=device)
Epoch: [0] [ 0/104] eta: 0:21:39 lr: 0.000053 loss: 2.5010 (2.5010) loss_classifier: 2.0748 (2.0748) loss_box_reg: 0.2147 (0.2147) loss_objectness: 0.1990 (0.1990) loss_rpn_box_reg: 0.0125 (0.0125) time: 12.4959 data: 6.5333 max mem: 3715 Epoch: [0] [ 10/104] eta: 0:03:02 lr: 0.000538 loss: 2.1539 (1.9279) loss_classifier: 1.6329 (1.4351) loss_box_reg: 0.2379 (0.2459) loss_objectness: 0.1990 (0.2296) loss_rpn_box_reg: 0.0125 (0.0173) time: 1.9377 data: 0.6165 max mem: 3875 Epoch: [0] [ 20/104] eta: 0:02:00 lr: 0.001023 loss: 1.2824 (1.5006) loss_classifier: 0.7298 (1.0260) loss_box_reg: 0.2868 (0.2820) loss_objectness: 0.1409 (0.1747) loss_rpn_box_reg: 0.0126 (0.0178) time: 0.8862 data: 0.0234 max mem: 3875 Epoch: [0] [ 30/104] eta: 0:01:33 lr: 0.001508 loss: 1.1755 (1.3630) loss_classifier: 0.6021 (0.8697) loss_box_reg: 0.3267 (0.3174) loss_objectness: 0.0952 (0.1566) loss_rpn_box_reg: 0.0171 (0.0194) time: 0.8888 data: 0.0215 max mem: 3875 Epoch: [0] [ 40/104] eta: 0:01:15 lr: 0.001993 loss: 0.9232 (1.2464) loss_classifier: 0.4607 (0.7632) loss_box_reg: 0.3912 (0.3269) loss_objectness: 0.0787 (0.1373) loss_rpn_box_reg: 0.0166 (0.0191) time: 0.8997 data: 0.0242 max mem: 3875 Epoch: [0] [ 50/104] eta: 0:01:00 lr: 0.002478 loss: 0.9030 (1.1955) loss_classifier: 0.4386 (0.7089) loss_box_reg: 0.3942 (0.3505) loss_objectness: 0.0389 (0.1173) loss_rpn_box_reg: 0.0166 (0.0187) time: 0.9106 data: 0.0249 max mem: 3875 Epoch: [0] [ 60/104] eta: 0:00:47 lr: 0.002963 loss: 0.9030 (1.1436) loss_classifier: 0.4437 (0.6605) loss_box_reg: 0.4301 (0.3621) loss_objectness: 0.0233 (0.1029) loss_rpn_box_reg: 0.0144 (0.0181) time: 0.9096 data: 0.0218 max mem: 3875 Epoch: [0] [ 70/104] eta: 0:00:36 lr: 0.003448 loss: 0.8739 (1.0972) loss_classifier: 0.4131 (0.6198) loss_box_reg: 0.4052 (0.3660) loss_objectness: 0.0335 (0.0940) loss_rpn_box_reg: 0.0122 (0.0173) time: 0.9186 data: 0.0233 max mem: 3875 Epoch: [0] [ 80/104] eta: 0:00:25 lr: 0.003933 loss: 0.7396 (1.0502) loss_classifier: 0.3503 (0.5853) loss_box_reg: 0.3653 (0.3625) loss_objectness: 0.0278 (0.0857) loss_rpn_box_reg: 0.0118 (0.0168) time: 0.9228 data: 0.0226 max mem: 3875 Epoch: [0] [ 90/104] eta: 0:00:14 lr: 0.004418 loss: 0.7196 (1.0105) loss_classifier: 0.3234 (0.5543) loss_box_reg: 0.3427 (0.3610) loss_objectness: 0.0169 (0.0786) loss_rpn_box_reg: 0.0118 (0.0166) time: 0.9814 data: 0.0218 max mem: 3875 Epoch: [0] [100/104] eta: 0:00:04 lr: 0.004903 loss: 0.6679 (0.9750) loss_classifier: 0.2767 (0.5279) loss_box_reg: 0.3131 (0.3563) loss_objectness: 0.0202 (0.0742) loss_rpn_box_reg: 0.0158 (0.0167) time: 0.9938 data: 0.0228 max mem: 3875 Epoch: [0] [103/104] eta: 0:00:01 lr: 0.005000 loss: 0.6873 (0.9721) loss_classifier: 0.2767 (0.5227) loss_box_reg: 0.3400 (0.3598) loss_objectness: 0.0206 (0.0729) loss_rpn_box_reg: 0.0162 (0.0167) time: 0.9364 data: 0.0207 max mem: 3875 Epoch: [0] Total time: 0:01:47 (1.0348 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:34 model_time: 0.4980 (0.4980) evaluator_time: 0.0921 (0.0921) time: 1.3389 data: 0.7408 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.3987 (0.4032) evaluator_time: 0.0276 (0.0543) time: 0.4832 data: 0.0204 max mem: 3875 Test: Total time: 0:00:13 (0.5164 s / it) Averaged stats: model_time: 0.3987 (0.4032) evaluator_time: 0.0276 (0.0543) Accumulating evaluation results... DONE (t=0.20s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.298 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.648 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.216 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.291 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.284 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.396 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.146 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.359 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.426 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.478 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.405 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.515 Epoch: [1] [ 0/104] eta: 0:02:56 lr: 0.005000 loss: 0.9491 (0.9491) loss_classifier: 0.4312 (0.4312) loss_box_reg: 0.4331 (0.4331) loss_objectness: 0.0605 (0.0605) loss_rpn_box_reg: 0.0243 (0.0243) time: 1.6972 data: 0.7045 max mem: 3875 Epoch: [1] [ 10/104] eta: 0:01:35 lr: 0.005000 loss: 0.6119 (0.5918) loss_classifier: 0.2030 (0.2291) loss_box_reg: 0.3582 (0.3184) loss_objectness: 0.0207 (0.0286) loss_rpn_box_reg: 0.0142 (0.0157) time: 1.0185 data: 0.0819 max mem: 3875 Epoch: [1] [ 20/104] eta: 0:01:22 lr: 0.005000 loss: 0.5908 (0.5789) loss_classifier: 0.2030 (0.2234) loss_box_reg: 0.3284 (0.3183) loss_objectness: 0.0156 (0.0229) loss_rpn_box_reg: 0.0127 (0.0143) time: 0.9470 data: 0.0199 max mem: 3875 Epoch: [1] [ 30/104] eta: 0:01:12 lr: 0.005000 loss: 0.5204 (0.5559) loss_classifier: 0.1771 (0.2077) loss_box_reg: 0.3169 (0.3159) loss_objectness: 0.0130 (0.0195) loss_rpn_box_reg: 0.0101 (0.0127) time: 0.9553 data: 0.0223 max mem: 3875 Epoch: [1] [ 40/104] eta: 0:01:02 lr: 0.005000 loss: 0.4952 (0.5393) loss_classifier: 0.1601 (0.1970) loss_box_reg: 0.2924 (0.3125) loss_objectness: 0.0105 (0.0175) loss_rpn_box_reg: 0.0084 (0.0124) time: 0.9706 data: 0.0242 max mem: 3875 Epoch: [1] [ 50/104] eta: 0:00:52 lr: 0.005000 loss: 0.4952 (0.5291) loss_classifier: 0.1601 (0.1904) loss_box_reg: 0.3116 (0.3071) loss_objectness: 0.0105 (0.0193) loss_rpn_box_reg: 0.0091 (0.0123) time: 0.9722 data: 0.0228 max mem: 3875 Epoch: [1] [ 60/104] eta: 0:00:43 lr: 0.005000 loss: 0.4953 (0.5243) loss_classifier: 0.1667 (0.1868) loss_box_reg: 0.3224 (0.3079) loss_objectness: 0.0101 (0.0176) loss_rpn_box_reg: 0.0093 (0.0120) time: 0.9808 data: 0.0234 max mem: 3875 Epoch: [1] [ 70/104] eta: 0:00:33 lr: 0.005000 loss: 0.5289 (0.5274) loss_classifier: 0.1667 (0.1846) loss_box_reg: 0.3381 (0.3131) loss_objectness: 0.0101 (0.0177) loss_rpn_box_reg: 0.0099 (0.0120) time: 0.9979 data: 0.0247 max mem: 3875 Epoch: [1] [ 80/104] eta: 0:00:23 lr: 0.005000 loss: 0.5120 (0.5213) loss_classifier: 0.1708 (0.1816) loss_box_reg: 0.3021 (0.3107) loss_objectness: 0.0116 (0.0169) loss_rpn_box_reg: 0.0117 (0.0121) time: 1.0052 data: 0.0230 max mem: 3875 Epoch: [1] [ 90/104] eta: 0:00:13 lr: 0.005000 loss: 0.5059 (0.5155) loss_classifier: 0.1356 (0.1771) loss_box_reg: 0.2882 (0.3094) loss_objectness: 0.0108 (0.0162) loss_rpn_box_reg: 0.0123 (0.0128) time: 1.0054 data: 0.0231 max mem: 3875 Epoch: [1] [100/104] eta: 0:00:03 lr: 0.005000 loss: 0.4005 (0.5019) loss_classifier: 0.1111 (0.1701) loss_box_reg: 0.2583 (0.3029) loss_objectness: 0.0071 (0.0164) loss_rpn_box_reg: 0.0104 (0.0125) time: 1.0041 data: 0.0235 max mem: 3875 Epoch: [1] [103/104] eta: 0:00:00 lr: 0.005000 loss: 0.3574 (0.5018) loss_classifier: 0.1061 (0.1696) loss_box_reg: 0.2464 (0.3035) loss_objectness: 0.0071 (0.0163) loss_rpn_box_reg: 0.0104 (0.0125) time: 0.9997 data: 0.0216 max mem: 3875 Epoch: [1] Total time: 0:01:42 (0.9901 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:39 model_time: 0.4493 (0.4493) evaluator_time: 0.0767 (0.0767) time: 1.5286 data: 0.9966 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4149 (0.4171) evaluator_time: 0.0226 (0.0432) time: 0.4765 data: 0.0200 max mem: 3875 Test: Total time: 0:00:13 (0.5298 s / it) Averaged stats: model_time: 0.4149 (0.4171) evaluator_time: 0.0226 (0.0432) Accumulating evaluation results... DONE (t=0.16s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.404 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.851 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.300 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.373 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.443 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.410 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.173 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.444 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.511 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.442 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.524 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.518 Epoch: [2] [ 0/104] eta: 0:03:16 lr: 0.005000 loss: 0.2529 (0.2529) loss_classifier: 0.0738 (0.0738) loss_box_reg: 0.1640 (0.1640) loss_objectness: 0.0093 (0.0093) loss_rpn_box_reg: 0.0059 (0.0059) time: 1.8854 data: 0.8509 max mem: 3875 Epoch: [2] [ 10/104] eta: 0:01:42 lr: 0.005000 loss: 0.3099 (0.3755) loss_classifier: 0.0912 (0.1106) loss_box_reg: 0.1996 (0.2446) loss_objectness: 0.0090 (0.0085) loss_rpn_box_reg: 0.0101 (0.0118) time: 1.0955 data: 0.0964 max mem: 3875 Epoch: [2] [ 20/104] eta: 0:01:29 lr: 0.005000 loss: 0.3128 (0.3552) loss_classifier: 0.0937 (0.1054) loss_box_reg: 0.2095 (0.2321) loss_objectness: 0.0053 (0.0076) loss_rpn_box_reg: 0.0081 (0.0101) time: 1.0247 data: 0.0217 max mem: 3875 Epoch: [2] [ 30/104] eta: 0:01:17 lr: 0.005000 loss: 0.3433 (0.3588) loss_classifier: 0.0937 (0.1049) loss_box_reg: 0.2423 (0.2373) loss_objectness: 0.0041 (0.0068) loss_rpn_box_reg: 0.0075 (0.0098) time: 1.0278 data: 0.0210 max mem: 3875 Epoch: [2] [ 40/104] eta: 0:01:06 lr: 0.005000 loss: 0.3896 (0.3662) loss_classifier: 0.0940 (0.1049) loss_box_reg: 0.2579 (0.2449) loss_objectness: 0.0050 (0.0066) loss_rpn_box_reg: 0.0089 (0.0098) time: 1.0151 data: 0.0201 max mem: 3875 Epoch: [2] [ 50/104] eta: 0:00:55 lr: 0.005000 loss: 0.3336 (0.3485) loss_classifier: 0.0760 (0.0991) loss_box_reg: 0.2311 (0.2342) loss_objectness: 0.0036 (0.0061) loss_rpn_box_reg: 0.0072 (0.0092) time: 0.9994 data: 0.0212 max mem: 3875 Epoch: [2] [ 60/104] eta: 0:00:45 lr: 0.005000 loss: 0.2878 (0.3424) loss_classifier: 0.0715 (0.0962) loss_box_reg: 0.1954 (0.2317) loss_objectness: 0.0022 (0.0056) loss_rpn_box_reg: 0.0061 (0.0088) time: 0.9889 data: 0.0225 max mem: 3875 Epoch: [2] [ 70/104] eta: 0:00:34 lr: 0.005000 loss: 0.3079 (0.3472) loss_classifier: 0.0890 (0.0980) loss_box_reg: 0.2112 (0.2347) loss_objectness: 0.0021 (0.0058) loss_rpn_box_reg: 0.0065 (0.0087) time: 0.9809 data: 0.0223 max mem: 3875 Epoch: [2] [ 80/104] eta: 0:00:24 lr: 0.005000 loss: 0.3821 (0.3515) loss_classifier: 0.1129 (0.0994) loss_box_reg: 0.2273 (0.2376) loss_objectness: 0.0038 (0.0056) loss_rpn_box_reg: 0.0073 (0.0088) time: 0.9853 data: 0.0229 max mem: 3875 Epoch: [2] [ 90/104] eta: 0:00:14 lr: 0.005000 loss: 0.4020 (0.3559) loss_classifier: 0.1104 (0.0998) loss_box_reg: 0.2543 (0.2404) loss_objectness: 0.0042 (0.0059) loss_rpn_box_reg: 0.0096 (0.0097) time: 0.9994 data: 0.0262 max mem: 3875 Epoch: [2] [100/104] eta: 0:00:04 lr: 0.005000 loss: 0.3498 (0.3545) loss_classifier: 0.0948 (0.1001) loss_box_reg: 0.2290 (0.2388) loss_objectness: 0.0056 (0.0059) loss_rpn_box_reg: 0.0096 (0.0098) time: 1.0009 data: 0.0244 max mem: 3875 Epoch: [2] [103/104] eta: 0:00:01 lr: 0.005000 loss: 0.3488 (0.3544) loss_classifier: 0.0948 (0.1005) loss_box_reg: 0.2203 (0.2382) loss_objectness: 0.0059 (0.0059) loss_rpn_box_reg: 0.0087 (0.0098) time: 1.0036 data: 0.0246 max mem: 3875 Epoch: [2] Total time: 0:01:45 (1.0130 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:35 model_time: 0.5247 (0.5247) evaluator_time: 0.0676 (0.0676) time: 1.3611 data: 0.7346 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4185 (0.4221) evaluator_time: 0.0190 (0.0292) time: 0.4727 data: 0.0202 max mem: 3875 Test: Total time: 0:00:13 (0.5123 s / it) Averaged stats: model_time: 0.4185 (0.4221) evaluator_time: 0.0190 (0.0292) Accumulating evaluation results... DONE (t=0.21s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.427 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.879 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.365 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.407 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.443 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.493 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.196 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.471 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.528 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.490 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.513 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.582 Epoch: [3] [ 0/104] eta: 0:03:14 lr: 0.000500 loss: 0.3512 (0.3512) loss_classifier: 0.0984 (0.0984) loss_box_reg: 0.2405 (0.2405) loss_objectness: 0.0026 (0.0026) loss_rpn_box_reg: 0.0097 (0.0097) time: 1.8720 data: 0.8414 max mem: 3875 Epoch: [3] [ 10/104] eta: 0:01:43 lr: 0.000500 loss: 0.3512 (0.3440) loss_classifier: 0.0903 (0.0885) loss_box_reg: 0.2405 (0.2414) loss_objectness: 0.0040 (0.0053) loss_rpn_box_reg: 0.0098 (0.0089) time: 1.1019 data: 0.0929 max mem: 3875 Epoch: [3] [ 20/104] eta: 0:01:30 lr: 0.000500 loss: 0.3133 (0.3273) loss_classifier: 0.0721 (0.0844) loss_box_reg: 0.2179 (0.2293) loss_objectness: 0.0032 (0.0041) loss_rpn_box_reg: 0.0085 (0.0095) time: 1.0327 data: 0.0198 max mem: 3875 Epoch: [3] [ 30/104] eta: 0:01:18 lr: 0.000500 loss: 0.2674 (0.3052) loss_classifier: 0.0729 (0.0806) loss_box_reg: 0.1863 (0.2121) loss_objectness: 0.0025 (0.0038) loss_rpn_box_reg: 0.0073 (0.0087) time: 1.0332 data: 0.0230 max mem: 3875 Epoch: [3] [ 40/104] eta: 0:01:06 lr: 0.000500 loss: 0.2504 (0.2893) loss_classifier: 0.0707 (0.0784) loss_box_reg: 0.1596 (0.1995) loss_objectness: 0.0024 (0.0034) loss_rpn_box_reg: 0.0056 (0.0080) time: 1.0149 data: 0.0229 max mem: 3875 Epoch: [3] [ 50/104] eta: 0:00:55 lr: 0.000500 loss: 0.2161 (0.2790) loss_classifier: 0.0646 (0.0765) loss_box_reg: 0.1435 (0.1916) loss_objectness: 0.0024 (0.0033) loss_rpn_box_reg: 0.0054 (0.0076) time: 0.9912 data: 0.0209 max mem: 3875 Epoch: [3] [ 60/104] eta: 0:00:45 lr: 0.000500 loss: 0.2449 (0.2840) loss_classifier: 0.0796 (0.0787) loss_box_reg: 0.1637 (0.1942) loss_objectness: 0.0030 (0.0034) loss_rpn_box_reg: 0.0060 (0.0077) time: 0.9837 data: 0.0226 max mem: 3875 Epoch: [3] [ 70/104] eta: 0:00:34 lr: 0.000500 loss: 0.2423 (0.2732) loss_classifier: 0.0679 (0.0758) loss_box_reg: 0.1603 (0.1866) loss_objectness: 0.0030 (0.0033) loss_rpn_box_reg: 0.0055 (0.0075) time: 0.9868 data: 0.0245 max mem: 3875 Epoch: [3] [ 80/104] eta: 0:00:24 lr: 0.000500 loss: 0.2168 (0.2685) loss_classifier: 0.0586 (0.0745) loss_box_reg: 0.1522 (0.1834) loss_objectness: 0.0022 (0.0032) loss_rpn_box_reg: 0.0044 (0.0074) time: 0.9822 data: 0.0226 max mem: 3875 Epoch: [3] [ 90/104] eta: 0:00:14 lr: 0.000500 loss: 0.2075 (0.2656) loss_classifier: 0.0661 (0.0737) loss_box_reg: 0.1524 (0.1815) loss_objectness: 0.0014 (0.0031) loss_rpn_box_reg: 0.0058 (0.0073) time: 0.9883 data: 0.0218 max mem: 3875 Epoch: [3] [100/104] eta: 0:00:04 lr: 0.000500 loss: 0.2281 (0.2643) loss_classifier: 0.0661 (0.0741) loss_box_reg: 0.1553 (0.1799) loss_objectness: 0.0020 (0.0030) loss_rpn_box_reg: 0.0062 (0.0073) time: 1.0008 data: 0.0222 max mem: 3875 Epoch: [3] [103/104] eta: 0:00:01 lr: 0.000500 loss: 0.2375 (0.2639) loss_classifier: 0.0669 (0.0740) loss_box_reg: 0.1607 (0.1795) loss_objectness: 0.0022 (0.0030) loss_rpn_box_reg: 0.0062 (0.0073) time: 0.9988 data: 0.0212 max mem: 3875 Epoch: [3] Total time: 0:01:45 (1.0120 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:38 model_time: 0.4865 (0.4865) evaluator_time: 0.0672 (0.0672) time: 1.4759 data: 0.9003 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4198 (0.4218) evaluator_time: 0.0131 (0.0320) time: 0.4808 data: 0.0219 max mem: 3875 Test: Total time: 0:00:13 (0.5194 s / it) Averaged stats: model_time: 0.4198 (0.4218) evaluator_time: 0.0131 (0.0320) Accumulating evaluation results... DONE (t=0.09s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.534 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.915 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.552 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.488 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.530 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.595 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.229 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.537 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.614 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.556 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.596 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.663 Epoch: [4] [ 0/104] eta: 0:03:04 lr: 0.000500 loss: 0.2486 (0.2486) loss_classifier: 0.0577 (0.0577) loss_box_reg: 0.1808 (0.1808) loss_objectness: 0.0028 (0.0028) loss_rpn_box_reg: 0.0073 (0.0073) time: 1.7771 data: 0.7311 max mem: 3875 Epoch: [4] [ 10/104] eta: 0:01:43 lr: 0.000500 loss: 0.2155 (0.2058) loss_classifier: 0.0577 (0.0601) loss_box_reg: 0.1410 (0.1385) loss_objectness: 0.0026 (0.0025) loss_rpn_box_reg: 0.0030 (0.0047) time: 1.0965 data: 0.0827 max mem: 3875 Epoch: [4] [ 20/104] eta: 0:01:29 lr: 0.000500 loss: 0.1932 (0.2172) loss_classifier: 0.0612 (0.0639) loss_box_reg: 0.1371 (0.1459) loss_objectness: 0.0018 (0.0024) loss_rpn_box_reg: 0.0040 (0.0050) time: 1.0333 data: 0.0197 max mem: 3875 Epoch: [4] [ 30/104] eta: 0:01:17 lr: 0.000500 loss: 0.2339 (0.2230) loss_classifier: 0.0727 (0.0653) loss_box_reg: 0.1525 (0.1502) loss_objectness: 0.0017 (0.0021) loss_rpn_box_reg: 0.0047 (0.0054) time: 1.0278 data: 0.0210 max mem: 3875 Epoch: [4] [ 40/104] eta: 0:01:06 lr: 0.000500 loss: 0.2130 (0.2196) loss_classifier: 0.0633 (0.0639) loss_box_reg: 0.1423 (0.1482) loss_objectness: 0.0013 (0.0021) loss_rpn_box_reg: 0.0050 (0.0054) time: 1.0089 data: 0.0217 max mem: 3875 Epoch: [4] [ 50/104] eta: 0:00:55 lr: 0.000500 loss: 0.2210 (0.2269) loss_classifier: 0.0633 (0.0647) loss_box_reg: 0.1510 (0.1542) loss_objectness: 0.0013 (0.0021) loss_rpn_box_reg: 0.0058 (0.0060) time: 0.9999 data: 0.0236 max mem: 3875 Epoch: [4] [ 60/104] eta: 0:00:44 lr: 0.000500 loss: 0.2511 (0.2310) loss_classifier: 0.0637 (0.0654) loss_box_reg: 0.1642 (0.1573) loss_objectness: 0.0013 (0.0021) loss_rpn_box_reg: 0.0073 (0.0062) time: 0.9870 data: 0.0227 max mem: 3875 Epoch: [4] [ 70/104] eta: 0:00:34 lr: 0.000500 loss: 0.2360 (0.2319) loss_classifier: 0.0629 (0.0651) loss_box_reg: 0.1642 (0.1585) loss_objectness: 0.0014 (0.0020) loss_rpn_box_reg: 0.0065 (0.0062) time: 0.9781 data: 0.0222 max mem: 3875 Epoch: [4] [ 80/104] eta: 0:00:24 lr: 0.000500 loss: 0.2556 (0.2351) loss_classifier: 0.0711 (0.0661) loss_box_reg: 0.1797 (0.1603) loss_objectness: 0.0022 (0.0023) loss_rpn_box_reg: 0.0065 (0.0064) time: 0.9863 data: 0.0230 max mem: 3875 Epoch: [4] [ 90/104] eta: 0:00:14 lr: 0.000500 loss: 0.2548 (0.2330) loss_classifier: 0.0719 (0.0656) loss_box_reg: 0.1665 (0.1588) loss_objectness: 0.0020 (0.0022) loss_rpn_box_reg: 0.0059 (0.0063) time: 0.9923 data: 0.0226 max mem: 3875 Epoch: [4] [100/104] eta: 0:00:04 lr: 0.000500 loss: 0.2291 (0.2333) loss_classifier: 0.0654 (0.0661) loss_box_reg: 0.1484 (0.1588) loss_objectness: 0.0011 (0.0021) loss_rpn_box_reg: 0.0048 (0.0063) time: 0.9973 data: 0.0218 max mem: 3875 Epoch: [4] [103/104] eta: 0:00:01 lr: 0.000500 loss: 0.2299 (0.2335) loss_classifier: 0.0654 (0.0661) loss_box_reg: 0.1570 (0.1588) loss_objectness: 0.0011 (0.0022) loss_rpn_box_reg: 0.0042 (0.0065) time: 0.9997 data: 0.0221 max mem: 3875 Epoch: [4] Total time: 0:01:45 (1.0115 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:56 model_time: 0.4848 (0.4848) evaluator_time: 0.1848 (0.1848) time: 2.1756 data: 1.4844 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4164 (0.4193) evaluator_time: 0.0129 (0.0284) time: 0.4625 data: 0.0184 max mem: 3875 Test: Total time: 0:00:13 (0.5355 s / it) Averaged stats: model_time: 0.4164 (0.4193) evaluator_time: 0.0129 (0.0284) Accumulating evaluation results... DONE (t=0.16s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.917 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.576 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.613 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.540 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.615 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.561 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Epoch: [5] [ 0/104] eta: 0:04:26 lr: 0.000500 loss: 0.3329 (0.3329) loss_classifier: 0.0899 (0.0899) loss_box_reg: 0.2281 (0.2281) loss_objectness: 0.0012 (0.0012) loss_rpn_box_reg: 0.0139 (0.0139) time: 2.5591 data: 1.2204 max mem: 3875 Epoch: [5] [ 10/104] eta: 0:01:49 lr: 0.000500 loss: 0.2360 (0.2425) loss_classifier: 0.0729 (0.0686) loss_box_reg: 0.1591 (0.1651) loss_objectness: 0.0020 (0.0025) loss_rpn_box_reg: 0.0043 (0.0063) time: 1.1659 data: 0.1310 max mem: 3875 Epoch: [5] [ 20/104] eta: 0:01:32 lr: 0.000500 loss: 0.2198 (0.2314) loss_classifier: 0.0586 (0.0661) loss_box_reg: 0.1513 (0.1563) loss_objectness: 0.0018 (0.0025) loss_rpn_box_reg: 0.0047 (0.0065) time: 1.0331 data: 0.0218 max mem: 3875 Epoch: [5] [ 30/104] eta: 0:01:20 lr: 0.000500 loss: 0.2059 (0.2168) loss_classifier: 0.0529 (0.0617) loss_box_reg: 0.1293 (0.1472) loss_objectness: 0.0017 (0.0022) loss_rpn_box_reg: 0.0047 (0.0056) time: 1.0350 data: 0.0247 max mem: 3875 Epoch: [5] [ 40/104] eta: 0:01:07 lr: 0.000500 loss: 0.2017 (0.2115) loss_classifier: 0.0542 (0.0598) loss_box_reg: 0.1387 (0.1441) loss_objectness: 0.0009 (0.0021) loss_rpn_box_reg: 0.0037 (0.0055) time: 1.0144 data: 0.0251 max mem: 3875 Epoch: [5] [ 50/104] eta: 0:00:56 lr: 0.000500 loss: 0.2017 (0.2092) loss_classifier: 0.0527 (0.0587) loss_box_reg: 0.1387 (0.1427) loss_objectness: 0.0011 (0.0021) loss_rpn_box_reg: 0.0047 (0.0057) time: 0.9947 data: 0.0240 max mem: 3875 Epoch: [5] [ 60/104] eta: 0:00:45 lr: 0.000500 loss: 0.2068 (0.2098) loss_classifier: 0.0559 (0.0589) loss_box_reg: 0.1391 (0.1430) loss_objectness: 0.0020 (0.0021) loss_rpn_box_reg: 0.0048 (0.0057) time: 0.9891 data: 0.0261 max mem: 3875 Epoch: [5] [ 70/104] eta: 0:00:35 lr: 0.000500 loss: 0.2366 (0.2142) loss_classifier: 0.0650 (0.0605) loss_box_reg: 0.1596 (0.1456) loss_objectness: 0.0018 (0.0022) loss_rpn_box_reg: 0.0058 (0.0060) time: 0.9862 data: 0.0246 max mem: 3875 Epoch: [5] [ 80/104] eta: 0:00:24 lr: 0.000500 loss: 0.2242 (0.2169) loss_classifier: 0.0663 (0.0612) loss_box_reg: 0.1553 (0.1477) loss_objectness: 0.0015 (0.0021) loss_rpn_box_reg: 0.0058 (0.0059) time: 0.9817 data: 0.0216 max mem: 3875 Epoch: [5] [ 90/104] eta: 0:00:14 lr: 0.000500 loss: 0.2099 (0.2160) loss_classifier: 0.0635 (0.0611) loss_box_reg: 0.1410 (0.1470) loss_objectness: 0.0010 (0.0021) loss_rpn_box_reg: 0.0046 (0.0059) time: 0.9899 data: 0.0235 max mem: 3875 Epoch: [5] [100/104] eta: 0:00:04 lr: 0.000500 loss: 0.2310 (0.2225) loss_classifier: 0.0614 (0.0622) loss_box_reg: 0.1553 (0.1520) loss_objectness: 0.0022 (0.0023) loss_rpn_box_reg: 0.0050 (0.0061) time: 1.0036 data: 0.0247 max mem: 3875 Epoch: [5] [103/104] eta: 0:00:01 lr: 0.000500 loss: 0.2390 (0.2231) loss_classifier: 0.0644 (0.0622) loss_box_reg: 0.1553 (0.1524) loss_objectness: 0.0025 (0.0023) loss_rpn_box_reg: 0.0050 (0.0062) time: 1.0010 data: 0.0226 max mem: 3875 Epoch: [5] Total time: 0:01:46 (1.0203 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:53 model_time: 0.4563 (0.4563) evaluator_time: 0.0650 (0.0650) time: 2.0548 data: 1.5238 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4182 (0.4190) evaluator_time: 0.0120 (0.0300) time: 0.4740 data: 0.0207 max mem: 3875 Test: Total time: 0:00:13 (0.5384 s / it) Averaged stats: model_time: 0.4182 (0.4190) evaluator_time: 0.0120 (0.0300) Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.543 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.928 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.561 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.477 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.544 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.635 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.234 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.610 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.541 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.597 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.678 Epoch: [6] [ 0/104] eta: 0:03:10 lr: 0.000050 loss: 0.1748 (0.1748) loss_classifier: 0.0497 (0.0497) loss_box_reg: 0.1187 (0.1187) loss_objectness: 0.0005 (0.0005) loss_rpn_box_reg: 0.0059 (0.0059) time: 1.8283 data: 0.7444 max mem: 3875 Epoch: [6] [ 10/104] eta: 0:01:43 lr: 0.000050 loss: 0.2450 (0.2322) loss_classifier: 0.0689 (0.0688) loss_box_reg: 0.1692 (0.1551) loss_objectness: 0.0016 (0.0019) loss_rpn_box_reg: 0.0063 (0.0064) time: 1.0963 data: 0.0858 max mem: 3875 Epoch: [6] [ 20/104] eta: 0:01:29 lr: 0.000050 loss: 0.2194 (0.2229) loss_classifier: 0.0646 (0.0635) loss_box_reg: 0.1513 (0.1514) loss_objectness: 0.0016 (0.0021) loss_rpn_box_reg: 0.0063 (0.0060) time: 1.0323 data: 0.0208 max mem: 3875 Epoch: [6] [ 30/104] eta: 0:01:17 lr: 0.000050 loss: 0.2048 (0.2163) loss_classifier: 0.0552 (0.0611) loss_box_reg: 0.1435 (0.1477) loss_objectness: 0.0011 (0.0020) loss_rpn_box_reg: 0.0041 (0.0055) time: 1.0301 data: 0.0215 max mem: 3875 Epoch: [6] [ 40/104] eta: 0:01:06 lr: 0.000050 loss: 0.1756 (0.2070) loss_classifier: 0.0488 (0.0574) loss_box_reg: 0.1240 (0.1425) loss_objectness: 0.0011 (0.0018) loss_rpn_box_reg: 0.0042 (0.0054) time: 1.0108 data: 0.0221 max mem: 3875 Epoch: [6] [ 50/104] eta: 0:00:55 lr: 0.000050 loss: 0.1981 (0.2085) loss_classifier: 0.0522 (0.0581) loss_box_reg: 0.1356 (0.1429) loss_objectness: 0.0009 (0.0019) loss_rpn_box_reg: 0.0046 (0.0056) time: 0.9953 data: 0.0228 max mem: 3875 Epoch: [6] [ 60/104] eta: 0:00:44 lr: 0.000050 loss: 0.2118 (0.2096) loss_classifier: 0.0613 (0.0586) loss_box_reg: 0.1436 (0.1435) loss_objectness: 0.0013 (0.0019) loss_rpn_box_reg: 0.0044 (0.0057) time: 0.9844 data: 0.0232 max mem: 3875 Epoch: [6] [ 70/104] eta: 0:00:34 lr: 0.000050 loss: 0.2118 (0.2107) loss_classifier: 0.0564 (0.0588) loss_box_reg: 0.1498 (0.1442) loss_objectness: 0.0008 (0.0019) loss_rpn_box_reg: 0.0053 (0.0057) time: 0.9816 data: 0.0236 max mem: 3875 Epoch: [6] [ 80/104] eta: 0:00:24 lr: 0.000050 loss: 0.2146 (0.2110) loss_classifier: 0.0564 (0.0587) loss_box_reg: 0.1474 (0.1444) loss_objectness: 0.0009 (0.0019) loss_rpn_box_reg: 0.0053 (0.0059) time: 0.9904 data: 0.0260 max mem: 3875 Epoch: [6] [ 90/104] eta: 0:00:14 lr: 0.000050 loss: 0.2061 (0.2114) loss_classifier: 0.0595 (0.0589) loss_box_reg: 0.1447 (0.1446) loss_objectness: 0.0014 (0.0019) loss_rpn_box_reg: 0.0050 (0.0059) time: 0.9995 data: 0.0258 max mem: 3875 Epoch: [6] [100/104] eta: 0:00:04 lr: 0.000050 loss: 0.2095 (0.2115) loss_classifier: 0.0598 (0.0592) loss_box_reg: 0.1422 (0.1445) loss_objectness: 0.0012 (0.0019) loss_rpn_box_reg: 0.0050 (0.0059) time: 0.9972 data: 0.0219 max mem: 3875 Epoch: [6] [103/104] eta: 0:00:01 lr: 0.000050 loss: 0.2186 (0.2117) loss_classifier: 0.0608 (0.0593) loss_box_reg: 0.1447 (0.1446) loss_objectness: 0.0013 (0.0019) loss_rpn_box_reg: 0.0050 (0.0059) time: 1.0005 data: 0.0220 max mem: 3875 Epoch: [6] Total time: 0:01:45 (1.0129 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:38 model_time: 0.4612 (0.4612) evaluator_time: 0.0828 (0.0828) time: 1.4617 data: 0.8888 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4153 (0.4177) evaluator_time: 0.0128 (0.0239) time: 0.4611 data: 0.0183 max mem: 3875 Test: Total time: 0:00:13 (0.5041 s / it) Averaged stats: model_time: 0.4153 (0.4177) evaluator_time: 0.0128 (0.0239) Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.548 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.928 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.573 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.482 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.549 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.640 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.236 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.613 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.546 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.599 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.685 Epoch: [7] [ 0/104] eta: 0:03:03 lr: 0.000050 loss: 0.3377 (0.3377) loss_classifier: 0.0893 (0.0893) loss_box_reg: 0.2355 (0.2355) loss_objectness: 0.0032 (0.0032) loss_rpn_box_reg: 0.0097 (0.0097) time: 1.7651 data: 0.7430 max mem: 3875 Epoch: [7] [ 10/104] eta: 0:01:42 lr: 0.000050 loss: 0.2396 (0.2352) loss_classifier: 0.0663 (0.0656) loss_box_reg: 0.1588 (0.1608) loss_objectness: 0.0014 (0.0023) loss_rpn_box_reg: 0.0060 (0.0066) time: 1.0899 data: 0.0860 max mem: 3875 Epoch: [7] [ 20/104] eta: 0:01:29 lr: 0.000050 loss: 0.1979 (0.2177) loss_classifier: 0.0567 (0.0607) loss_box_reg: 0.1329 (0.1488) loss_objectness: 0.0010 (0.0019) loss_rpn_box_reg: 0.0058 (0.0063) time: 1.0304 data: 0.0212 max mem: 3875 Epoch: [7] [ 30/104] eta: 0:01:17 lr: 0.000050 loss: 0.1922 (0.2192) loss_classifier: 0.0567 (0.0634) loss_box_reg: 0.1329 (0.1474) loss_objectness: 0.0010 (0.0018) loss_rpn_box_reg: 0.0049 (0.0066) time: 1.0332 data: 0.0228 max mem: 3875 Epoch: [7] [ 40/104] eta: 0:01:06 lr: 0.000050 loss: 0.2160 (0.2161) loss_classifier: 0.0579 (0.0619) loss_box_reg: 0.1450 (0.1458) loss_objectness: 0.0011 (0.0017) loss_rpn_box_reg: 0.0059 (0.0066) time: 1.0172 data: 0.0243 max mem: 3875 Epoch: [7] [ 50/104] eta: 0:00:55 lr: 0.000050 loss: 0.2120 (0.2142) loss_classifier: 0.0539 (0.0604) loss_box_reg: 0.1449 (0.1458) loss_objectness: 0.0011 (0.0017) loss_rpn_box_reg: 0.0053 (0.0062) time: 0.9945 data: 0.0235 max mem: 3875 Epoch: [7] [ 60/104] eta: 0:00:44 lr: 0.000050 loss: 0.1899 (0.2105) loss_classifier: 0.0537 (0.0594) loss_box_reg: 0.1309 (0.1433) loss_objectness: 0.0019 (0.0018) loss_rpn_box_reg: 0.0042 (0.0060) time: 0.9808 data: 0.0224 max mem: 3875 Epoch: [7] [ 70/104] eta: 0:00:34 lr: 0.000050 loss: 0.1954 (0.2118) loss_classifier: 0.0578 (0.0595) loss_box_reg: 0.1323 (0.1444) loss_objectness: 0.0016 (0.0018) loss_rpn_box_reg: 0.0052 (0.0061) time: 0.9809 data: 0.0232 max mem: 3875 Epoch: [7] [ 80/104] eta: 0:00:24 lr: 0.000050 loss: 0.2007 (0.2075) loss_classifier: 0.0570 (0.0583) loss_box_reg: 0.1323 (0.1416) loss_objectness: 0.0016 (0.0018) loss_rpn_box_reg: 0.0048 (0.0058) time: 0.9861 data: 0.0224 max mem: 3875 Epoch: [7] [ 90/104] eta: 0:00:14 lr: 0.000050 loss: 0.1722 (0.2061) loss_classifier: 0.0577 (0.0582) loss_box_reg: 0.1187 (0.1403) loss_objectness: 0.0011 (0.0018) loss_rpn_box_reg: 0.0042 (0.0058) time: 0.9900 data: 0.0216 max mem: 3875 Epoch: [7] [100/104] eta: 0:00:04 lr: 0.000050 loss: 0.2126 (0.2080) loss_classifier: 0.0583 (0.0585) loss_box_reg: 0.1433 (0.1418) loss_objectness: 0.0008 (0.0018) loss_rpn_box_reg: 0.0052 (0.0059) time: 0.9945 data: 0.0229 max mem: 3875 Epoch: [7] [103/104] eta: 0:00:01 lr: 0.000050 loss: 0.2127 (0.2091) loss_classifier: 0.0603 (0.0586) loss_box_reg: 0.1433 (0.1428) loss_objectness: 0.0008 (0.0018) loss_rpn_box_reg: 0.0050 (0.0059) time: 0.9928 data: 0.0218 max mem: 3875 Epoch: [7] Total time: 0:01:45 (1.0097 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:38 model_time: 0.5028 (0.5028) evaluator_time: 0.1567 (0.1567) time: 1.4818 data: 0.8029 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4149 (0.4197) evaluator_time: 0.0106 (0.0323) time: 0.4615 data: 0.0184 max mem: 3875 Test: Total time: 0:00:13 (0.5105 s / it) Averaged stats: model_time: 0.4149 (0.4197) evaluator_time: 0.0106 (0.0323) Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.547 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.928 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.580 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.486 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.547 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.627 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.234 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.543 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.613 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.549 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.599 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Epoch: [8] [ 0/104] eta: 0:03:03 lr: 0.000050 loss: 0.1904 (0.1904) loss_classifier: 0.0511 (0.0511) loss_box_reg: 0.1332 (0.1332) loss_objectness: 0.0031 (0.0031) loss_rpn_box_reg: 0.0030 (0.0030) time: 1.7656 data: 0.7269 max mem: 3875 Epoch: [8] [ 10/104] eta: 0:01:42 lr: 0.000050 loss: 0.1936 (0.2069) loss_classifier: 0.0489 (0.0585) loss_box_reg: 0.1332 (0.1405) loss_objectness: 0.0021 (0.0023) loss_rpn_box_reg: 0.0032 (0.0057) time: 1.0956 data: 0.0855 max mem: 3875 Epoch: [8] [ 20/104] eta: 0:01:29 lr: 0.000050 loss: 0.1984 (0.1999) loss_classifier: 0.0493 (0.0574) loss_box_reg: 0.1366 (0.1350) loss_objectness: 0.0012 (0.0021) loss_rpn_box_reg: 0.0042 (0.0055) time: 1.0346 data: 0.0230 max mem: 3875 Epoch: [8] [ 30/104] eta: 0:01:17 lr: 0.000050 loss: 0.2162 (0.2172) loss_classifier: 0.0580 (0.0618) loss_box_reg: 0.1442 (0.1470) loss_objectness: 0.0018 (0.0021) loss_rpn_box_reg: 0.0046 (0.0062) time: 1.0295 data: 0.0229 max mem: 3875 Epoch: [8] [ 40/104] eta: 0:01:06 lr: 0.000050 loss: 0.2084 (0.2106) loss_classifier: 0.0619 (0.0616) loss_box_reg: 0.1368 (0.1412) loss_objectness: 0.0013 (0.0020) loss_rpn_box_reg: 0.0042 (0.0057) time: 1.0076 data: 0.0210 max mem: 3875 Epoch: [8] [ 50/104] eta: 0:00:55 lr: 0.000050 loss: 0.2009 (0.2107) loss_classifier: 0.0622 (0.0618) loss_box_reg: 0.1270 (0.1413) loss_objectness: 0.0009 (0.0018) loss_rpn_box_reg: 0.0042 (0.0057) time: 0.9915 data: 0.0215 max mem: 3875 Epoch: [8] [ 60/104] eta: 0:00:44 lr: 0.000050 loss: 0.2150 (0.2111) loss_classifier: 0.0575 (0.0608) loss_box_reg: 0.1517 (0.1426) loss_objectness: 0.0011 (0.0018) loss_rpn_box_reg: 0.0052 (0.0059) time: 0.9845 data: 0.0227 max mem: 3875 Epoch: [8] [ 70/104] eta: 0:00:34 lr: 0.000050 loss: 0.2150 (0.2114) loss_classifier: 0.0575 (0.0605) loss_box_reg: 0.1531 (0.1431) loss_objectness: 0.0019 (0.0018) loss_rpn_box_reg: 0.0065 (0.0060) time: 0.9791 data: 0.0220 max mem: 3875 Epoch: [8] [ 80/104] eta: 0:00:24 lr: 0.000050 loss: 0.2228 (0.2130) loss_classifier: 0.0610 (0.0605) loss_box_reg: 0.1561 (0.1446) loss_objectness: 0.0020 (0.0018) loss_rpn_box_reg: 0.0068 (0.0061) time: 0.9812 data: 0.0217 max mem: 3875 Epoch: [8] [ 90/104] eta: 0:00:14 lr: 0.000050 loss: 0.2226 (0.2116) loss_classifier: 0.0574 (0.0602) loss_box_reg: 0.1549 (0.1436) loss_objectness: 0.0014 (0.0018) loss_rpn_box_reg: 0.0061 (0.0060) time: 0.9908 data: 0.0226 max mem: 3875 Epoch: [8] [100/104] eta: 0:00:04 lr: 0.000050 loss: 0.1706 (0.2097) loss_classifier: 0.0499 (0.0593) loss_box_reg: 0.1162 (0.1427) loss_objectness: 0.0010 (0.0018) loss_rpn_box_reg: 0.0048 (0.0059) time: 0.9941 data: 0.0213 max mem: 3875 Epoch: [8] [103/104] eta: 0:00:01 lr: 0.000050 loss: 0.1645 (0.2086) loss_classifier: 0.0468 (0.0588) loss_box_reg: 0.1145 (0.1422) loss_objectness: 0.0007 (0.0017) loss_rpn_box_reg: 0.0039 (0.0059) time: 0.9967 data: 0.0211 max mem: 3875 Epoch: [8] Total time: 0:01:44 (1.0086 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:36 model_time: 0.4546 (0.4546) evaluator_time: 0.0618 (0.0618) time: 1.3852 data: 0.8516 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4210 (0.4196) evaluator_time: 0.0127 (0.0233) time: 0.4646 data: 0.0182 max mem: 3875 Test: Total time: 0:00:13 (0.5037 s / it) Averaged stats: model_time: 0.4210 (0.4196) evaluator_time: 0.0127 (0.0233) Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.548 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.928 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.587 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.486 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.546 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.627 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.543 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.614 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.601 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Epoch: [9] [ 0/104] eta: 0:03:09 lr: 0.000005 loss: 0.1976 (0.1976) loss_classifier: 0.0576 (0.0576) loss_box_reg: 0.1314 (0.1314) loss_objectness: 0.0030 (0.0030) loss_rpn_box_reg: 0.0056 (0.0056) time: 1.8196 data: 0.7632 max mem: 3875 Epoch: [9] [ 10/104] eta: 0:01:43 lr: 0.000005 loss: 0.1904 (0.1900) loss_classifier: 0.0542 (0.0547) loss_box_reg: 0.1302 (0.1282) loss_objectness: 0.0014 (0.0020) loss_rpn_box_reg: 0.0046 (0.0051) time: 1.0982 data: 0.0892 max mem: 3875 Epoch: [9] [ 20/104] eta: 0:01:29 lr: 0.000005 loss: 0.1882 (0.1912) loss_classifier: 0.0529 (0.0547) loss_box_reg: 0.1255 (0.1301) loss_objectness: 0.0013 (0.0017) loss_rpn_box_reg: 0.0040 (0.0048) time: 1.0271 data: 0.0208 max mem: 3875 Epoch: [9] [ 30/104] eta: 0:01:17 lr: 0.000005 loss: 0.1947 (0.1902) loss_classifier: 0.0508 (0.0538) loss_box_reg: 0.1255 (0.1299) loss_objectness: 0.0012 (0.0016) loss_rpn_box_reg: 0.0040 (0.0049) time: 1.0192 data: 0.0215 max mem: 3875 Epoch: [9] [ 40/104] eta: 0:01:06 lr: 0.000005 loss: 0.1974 (0.1946) loss_classifier: 0.0534 (0.0546) loss_box_reg: 0.1315 (0.1329) loss_objectness: 0.0013 (0.0016) loss_rpn_box_reg: 0.0043 (0.0054) time: 1.0052 data: 0.0221 max mem: 3875 Epoch: [9] [ 50/104] eta: 0:00:55 lr: 0.000005 loss: 0.1927 (0.1957) loss_classifier: 0.0568 (0.0554) loss_box_reg: 0.1315 (0.1333) loss_objectness: 0.0012 (0.0015) loss_rpn_box_reg: 0.0052 (0.0055) time: 0.9902 data: 0.0203 max mem: 3875 Epoch: [9] [ 60/104] eta: 0:00:44 lr: 0.000005 loss: 0.2107 (0.1974) loss_classifier: 0.0577 (0.0556) loss_box_reg: 0.1355 (0.1346) loss_objectness: 0.0009 (0.0016) loss_rpn_box_reg: 0.0054 (0.0057) time: 0.9783 data: 0.0208 max mem: 3875 Epoch: [9] [ 70/104] eta: 0:00:34 lr: 0.000005 loss: 0.2091 (0.1986) loss_classifier: 0.0561 (0.0563) loss_box_reg: 0.1366 (0.1352) loss_objectness: 0.0008 (0.0016) loss_rpn_box_reg: 0.0043 (0.0055) time: 0.9819 data: 0.0225 max mem: 3875 Epoch: [9] [ 80/104] eta: 0:00:24 lr: 0.000005 loss: 0.2091 (0.2051) loss_classifier: 0.0641 (0.0586) loss_box_reg: 0.1418 (0.1391) loss_objectness: 0.0012 (0.0017) loss_rpn_box_reg: 0.0053 (0.0058) time: 0.9880 data: 0.0223 max mem: 3875 Epoch: [9] [ 90/104] eta: 0:00:14 lr: 0.000005 loss: 0.2247 (0.2080) loss_classifier: 0.0641 (0.0592) loss_box_reg: 0.1488 (0.1413) loss_objectness: 0.0014 (0.0017) loss_rpn_box_reg: 0.0055 (0.0058) time: 0.9888 data: 0.0214 max mem: 3875 Epoch: [9] [100/104] eta: 0:00:04 lr: 0.000005 loss: 0.2146 (0.2070) loss_classifier: 0.0593 (0.0586) loss_box_reg: 0.1465 (0.1409) loss_objectness: 0.0011 (0.0017) loss_rpn_box_reg: 0.0054 (0.0057) time: 0.9937 data: 0.0217 max mem: 3875 Epoch: [9] [103/104] eta: 0:00:01 lr: 0.000005 loss: 0.2110 (0.2080) loss_classifier: 0.0592 (0.0591) loss_box_reg: 0.1438 (0.1413) loss_objectness: 0.0010 (0.0017) loss_rpn_box_reg: 0.0053 (0.0059) time: 0.9943 data: 0.0213 max mem: 3875 Epoch: [9] Total time: 0:01:44 (1.0075 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:54 model_time: 0.5491 (0.5491) evaluator_time: 0.2153 (0.2153) time: 2.0892 data: 1.3075 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4136 (0.4206) evaluator_time: 0.0113 (0.0299) time: 0.4544 data: 0.0166 max mem: 3875 Test: Total time: 0:00:13 (0.5293 s / it) Averaged stats: model_time: 0.4136 (0.4206) evaluator_time: 0.0113 (0.0299) Accumulating evaluation results... DONE (t=0.14s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.548 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.928 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.587 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.486 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.547 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.627 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.543 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.615 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.601 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Epoch: [10] [ 0/104] eta: 0:04:01 lr: 0.000005 loss: 0.2751 (0.2751) loss_classifier: 0.0831 (0.0831) loss_box_reg: 0.1825 (0.1825) loss_objectness: 0.0013 (0.0013) loss_rpn_box_reg: 0.0083 (0.0083) time: 2.3237 data: 1.2064 max mem: 3875 Epoch: [10] [ 10/104] eta: 0:01:47 lr: 0.000005 loss: 0.2280 (0.2265) loss_classifier: 0.0661 (0.0682) loss_box_reg: 0.1609 (0.1499) loss_objectness: 0.0017 (0.0021) loss_rpn_box_reg: 0.0055 (0.0062) time: 1.1399 data: 0.1263 max mem: 3875 Epoch: [10] [ 20/104] eta: 0:01:31 lr: 0.000005 loss: 0.1922 (0.2111) loss_classifier: 0.0605 (0.0607) loss_box_reg: 0.1324 (0.1427) loss_objectness: 0.0014 (0.0019) loss_rpn_box_reg: 0.0047 (0.0058) time: 1.0319 data: 0.0200 max mem: 3875 Epoch: [10] [ 30/104] eta: 0:01:19 lr: 0.000005 loss: 0.2016 (0.2236) loss_classifier: 0.0590 (0.0650) loss_box_reg: 0.1326 (0.1502) loss_objectness: 0.0014 (0.0019) loss_rpn_box_reg: 0.0060 (0.0065) time: 1.0328 data: 0.0218 max mem: 3875 Epoch: [10] [ 40/104] eta: 0:01:07 lr: 0.000005 loss: 0.1926 (0.2149) loss_classifier: 0.0549 (0.0622) loss_box_reg: 0.1342 (0.1445) loss_objectness: 0.0009 (0.0018) loss_rpn_box_reg: 0.0041 (0.0065) time: 1.0111 data: 0.0215 max mem: 3875 Epoch: [10] [ 50/104] eta: 0:00:56 lr: 0.000005 loss: 0.1722 (0.2061) loss_classifier: 0.0491 (0.0592) loss_box_reg: 0.1142 (0.1391) loss_objectness: 0.0006 (0.0017) loss_rpn_box_reg: 0.0038 (0.0061) time: 0.9925 data: 0.0218 max mem: 3875 Epoch: [10] [ 60/104] eta: 0:00:45 lr: 0.000005 loss: 0.1990 (0.2091) loss_classifier: 0.0547 (0.0599) loss_box_reg: 0.1351 (0.1412) loss_objectness: 0.0018 (0.0018) loss_rpn_box_reg: 0.0056 (0.0061) time: 0.9838 data: 0.0221 max mem: 3875 Epoch: [10] [ 70/104] eta: 0:00:34 lr: 0.000005 loss: 0.2036 (0.2068) loss_classifier: 0.0572 (0.0591) loss_box_reg: 0.1417 (0.1401) loss_objectness: 0.0015 (0.0018) loss_rpn_box_reg: 0.0048 (0.0058) time: 0.9812 data: 0.0233 max mem: 3875 Epoch: [10] [ 80/104] eta: 0:00:24 lr: 0.000005 loss: 0.2014 (0.2067) loss_classifier: 0.0511 (0.0585) loss_box_reg: 0.1399 (0.1405) loss_objectness: 0.0014 (0.0019) loss_rpn_box_reg: 0.0036 (0.0058) time: 0.9775 data: 0.0225 max mem: 3875 Epoch: [10] [ 90/104] eta: 0:00:14 lr: 0.000005 loss: 0.2014 (0.2059) loss_classifier: 0.0546 (0.0583) loss_box_reg: 0.1412 (0.1400) loss_objectness: 0.0015 (0.0019) loss_rpn_box_reg: 0.0036 (0.0057) time: 0.9832 data: 0.0213 max mem: 3875 Epoch: [10] [100/104] eta: 0:00:04 lr: 0.000005 loss: 0.1887 (0.2061) loss_classifier: 0.0589 (0.0583) loss_box_reg: 0.1298 (0.1401) loss_objectness: 0.0015 (0.0019) loss_rpn_box_reg: 0.0035 (0.0058) time: 0.9930 data: 0.0208 max mem: 3875 Epoch: [10] [103/104] eta: 0:00:01 lr: 0.000005 loss: 0.1882 (0.2076) loss_classifier: 0.0546 (0.0587) loss_box_reg: 0.1339 (0.1411) loss_objectness: 0.0016 (0.0020) loss_rpn_box_reg: 0.0034 (0.0058) time: 0.9903 data: 0.0196 max mem: 3875 Epoch: [10] Total time: 0:01:45 (1.0130 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:38 model_time: 0.4692 (0.4692) evaluator_time: 0.0643 (0.0643) time: 1.4834 data: 0.9290 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4167 (0.4182) evaluator_time: 0.0112 (0.0283) time: 0.4725 data: 0.0199 max mem: 3875 Test: Total time: 0:00:13 (0.5124 s / it) Averaged stats: model_time: 0.4167 (0.4182) evaluator_time: 0.0112 (0.0283) Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.548 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.928 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.586 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.487 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.547 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.627 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.236 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.615 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.548 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Epoch: [11] [ 0/104] eta: 0:03:03 lr: 0.000005 loss: 0.1635 (0.1635) loss_classifier: 0.0396 (0.0396) loss_box_reg: 0.1188 (0.1188) loss_objectness: 0.0006 (0.0006) loss_rpn_box_reg: 0.0046 (0.0046) time: 1.7666 data: 0.7019 max mem: 3875 Epoch: [11] [ 10/104] eta: 0:01:42 lr: 0.000005 loss: 0.1990 (0.1852) loss_classifier: 0.0505 (0.0493) loss_box_reg: 0.1402 (0.1305) loss_objectness: 0.0007 (0.0008) loss_rpn_box_reg: 0.0045 (0.0046) time: 1.0910 data: 0.0818 max mem: 3875 Epoch: [11] [ 20/104] eta: 0:01:29 lr: 0.000005 loss: 0.1990 (0.1972) loss_classifier: 0.0505 (0.0548) loss_box_reg: 0.1385 (0.1357) loss_objectness: 0.0010 (0.0015) loss_rpn_box_reg: 0.0049 (0.0053) time: 1.0275 data: 0.0204 max mem: 3875 Epoch: [11] [ 30/104] eta: 0:01:17 lr: 0.000005 loss: 0.2375 (0.2158) loss_classifier: 0.0670 (0.0603) loss_box_reg: 0.1542 (0.1479) loss_objectness: 0.0018 (0.0020) loss_rpn_box_reg: 0.0060 (0.0056) time: 1.0214 data: 0.0209 max mem: 3875 Epoch: [11] [ 40/104] eta: 0:01:06 lr: 0.000005 loss: 0.2013 (0.2082) loss_classifier: 0.0544 (0.0581) loss_box_reg: 0.1448 (0.1425) loss_objectness: 0.0014 (0.0018) loss_rpn_box_reg: 0.0052 (0.0057) time: 1.0068 data: 0.0226 max mem: 3875 Epoch: [11] [ 50/104] eta: 0:00:55 lr: 0.000005 loss: 0.1670 (0.2043) loss_classifier: 0.0510 (0.0574) loss_box_reg: 0.1161 (0.1394) loss_objectness: 0.0013 (0.0019) loss_rpn_box_reg: 0.0046 (0.0056) time: 0.9946 data: 0.0237 max mem: 3875 Epoch: [11] [ 60/104] eta: 0:00:44 lr: 0.000005 loss: 0.1670 (0.2026) loss_classifier: 0.0510 (0.0570) loss_box_reg: 0.1170 (0.1380) loss_objectness: 0.0012 (0.0021) loss_rpn_box_reg: 0.0043 (0.0055) time: 0.9816 data: 0.0222 max mem: 3875 Epoch: [11] [ 70/104] eta: 0:00:34 lr: 0.000005 loss: 0.1836 (0.2035) loss_classifier: 0.0537 (0.0571) loss_box_reg: 0.1204 (0.1388) loss_objectness: 0.0008 (0.0020) loss_rpn_box_reg: 0.0040 (0.0056) time: 0.9792 data: 0.0227 max mem: 3875 Epoch: [11] [ 80/104] eta: 0:00:24 lr: 0.000005 loss: 0.2026 (0.2034) loss_classifier: 0.0572 (0.0570) loss_box_reg: 0.1380 (0.1388) loss_objectness: 0.0008 (0.0019) loss_rpn_box_reg: 0.0053 (0.0057) time: 0.9844 data: 0.0232 max mem: 3875 Epoch: [11] [ 90/104] eta: 0:00:14 lr: 0.000005 loss: 0.2026 (0.2047) loss_classifier: 0.0541 (0.0572) loss_box_reg: 0.1389 (0.1400) loss_objectness: 0.0010 (0.0019) loss_rpn_box_reg: 0.0053 (0.0057) time: 0.9930 data: 0.0228 max mem: 3875 Epoch: [11] [100/104] eta: 0:00:04 lr: 0.000005 loss: 0.1901 (0.2054) loss_classifier: 0.0549 (0.0579) loss_box_reg: 0.1389 (0.1400) loss_objectness: 0.0016 (0.0019) loss_rpn_box_reg: 0.0058 (0.0057) time: 0.9939 data: 0.0210 max mem: 3875 Epoch: [11] [103/104] eta: 0:00:01 lr: 0.000005 loss: 0.2141 (0.2069) loss_classifier: 0.0549 (0.0582) loss_box_reg: 0.1521 (0.1409) loss_objectness: 0.0016 (0.0019) loss_rpn_box_reg: 0.0065 (0.0058) time: 0.9972 data: 0.0210 max mem: 3875 Epoch: [11] Total time: 0:01:44 (1.0078 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:49 model_time: 0.4933 (0.4933) evaluator_time: 0.0623 (0.0623) time: 1.8962 data: 1.3249 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4157 (0.4191) evaluator_time: 0.0114 (0.0227) time: 0.4753 data: 0.0326 max mem: 3875 Test: Total time: 0:00:13 (0.5335 s / it) Averaged stats: model_time: 0.4157 (0.4191) evaluator_time: 0.0114 (0.0227) Accumulating evaluation results... DONE (t=0.14s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.549 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.928 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.585 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.487 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.547 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.627 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.236 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.615 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.548 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Epoch: [12] [ 0/104] eta: 0:03:26 lr: 0.000001 loss: 0.1430 (0.1430) loss_classifier: 0.0349 (0.0349) loss_box_reg: 0.1038 (0.1038) loss_objectness: 0.0007 (0.0007) loss_rpn_box_reg: 0.0036 (0.0036) time: 1.9831 data: 0.9518 max mem: 3875 Epoch: [12] [ 10/104] eta: 0:01:43 lr: 0.000001 loss: 0.2299 (0.2337) loss_classifier: 0.0665 (0.0664) loss_box_reg: 0.1570 (0.1600) loss_objectness: 0.0015 (0.0015) loss_rpn_box_reg: 0.0043 (0.0058) time: 1.1000 data: 0.1032 max mem: 3875 Epoch: [12] [ 20/104] eta: 0:01:29 lr: 0.000001 loss: 0.2113 (0.2083) loss_classifier: 0.0605 (0.0593) loss_box_reg: 0.1419 (0.1427) loss_objectness: 0.0012 (0.0013) loss_rpn_box_reg: 0.0047 (0.0051) time: 1.0202 data: 0.0198 max mem: 3875 Epoch: [12] [ 30/104] eta: 0:01:17 lr: 0.000001 loss: 0.1770 (0.2075) loss_classifier: 0.0544 (0.0600) loss_box_reg: 0.1148 (0.1406) loss_objectness: 0.0011 (0.0015) loss_rpn_box_reg: 0.0047 (0.0054) time: 1.0279 data: 0.0251 max mem: 3875 Epoch: [12] [ 40/104] eta: 0:01:06 lr: 0.000001 loss: 0.1848 (0.2008) loss_classifier: 0.0544 (0.0577) loss_box_reg: 0.1250 (0.1359) loss_objectness: 0.0015 (0.0017) loss_rpn_box_reg: 0.0047 (0.0053) time: 1.0148 data: 0.0253 max mem: 3875 Epoch: [12] [ 50/104] eta: 0:00:55 lr: 0.000001 loss: 0.1868 (0.2007) loss_classifier: 0.0520 (0.0573) loss_box_reg: 0.1283 (0.1362) loss_objectness: 0.0013 (0.0018) loss_rpn_box_reg: 0.0041 (0.0054) time: 0.9901 data: 0.0209 max mem: 3875 Epoch: [12] [ 60/104] eta: 0:00:44 lr: 0.000001 loss: 0.2124 (0.2051) loss_classifier: 0.0576 (0.0589) loss_box_reg: 0.1465 (0.1387) loss_objectness: 0.0012 (0.0018) loss_rpn_box_reg: 0.0057 (0.0057) time: 0.9809 data: 0.0218 max mem: 3875 Epoch: [12] [ 70/104] eta: 0:00:34 lr: 0.000001 loss: 0.2013 (0.2020) loss_classifier: 0.0571 (0.0577) loss_box_reg: 0.1352 (0.1369) loss_objectness: 0.0012 (0.0018) loss_rpn_box_reg: 0.0057 (0.0056) time: 0.9821 data: 0.0221 max mem: 3875 Epoch: [12] [ 80/104] eta: 0:00:24 lr: 0.000001 loss: 0.1894 (0.2041) loss_classifier: 0.0489 (0.0579) loss_box_reg: 0.1314 (0.1388) loss_objectness: 0.0008 (0.0017) loss_rpn_box_reg: 0.0042 (0.0057) time: 0.9809 data: 0.0210 max mem: 3875 Epoch: [12] [ 90/104] eta: 0:00:14 lr: 0.000001 loss: 0.2155 (0.2062) loss_classifier: 0.0497 (0.0582) loss_box_reg: 0.1516 (0.1404) loss_objectness: 0.0008 (0.0018) loss_rpn_box_reg: 0.0044 (0.0058) time: 0.9864 data: 0.0212 max mem: 3875 Epoch: [12] [100/104] eta: 0:00:04 lr: 0.000001 loss: 0.1985 (0.2077) loss_classifier: 0.0562 (0.0584) loss_box_reg: 0.1371 (0.1418) loss_objectness: 0.0011 (0.0017) loss_rpn_box_reg: 0.0056 (0.0058) time: 0.9979 data: 0.0213 max mem: 3875 Epoch: [12] [103/104] eta: 0:00:01 lr: 0.000001 loss: 0.1981 (0.2069) loss_classifier: 0.0543 (0.0583) loss_box_reg: 0.1312 (0.1411) loss_objectness: 0.0011 (0.0017) loss_rpn_box_reg: 0.0056 (0.0058) time: 0.9971 data: 0.0205 max mem: 3875 Epoch: [12] Total time: 0:01:44 (1.0093 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:36 model_time: 0.4881 (0.4881) evaluator_time: 0.0641 (0.0641) time: 1.4223 data: 0.8640 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4172 (0.4209) evaluator_time: 0.0115 (0.0287) time: 0.4648 data: 0.0180 max mem: 3875 Test: Total time: 0:00:13 (0.5111 s / it) Averaged stats: model_time: 0.4172 (0.4209) evaluator_time: 0.0115 (0.0287) Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.549 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.928 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.585 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.487 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.547 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.627 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.236 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.615 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.548 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Epoch: [13] [ 0/104] eta: 0:03:02 lr: 0.000001 loss: 0.2508 (0.2508) loss_classifier: 0.0759 (0.0759) loss_box_reg: 0.1537 (0.1537) loss_objectness: 0.0072 (0.0072) loss_rpn_box_reg: 0.0140 (0.0140) time: 1.7528 data: 0.6909 max mem: 3875 Epoch: [13] [ 10/104] eta: 0:01:42 lr: 0.000001 loss: 0.1942 (0.2034) loss_classifier: 0.0629 (0.0602) loss_box_reg: 0.1263 (0.1356) loss_objectness: 0.0011 (0.0017) loss_rpn_box_reg: 0.0049 (0.0059) time: 1.0934 data: 0.0841 max mem: 3875 Epoch: [13] [ 20/104] eta: 0:01:29 lr: 0.000001 loss: 0.2218 (0.2154) loss_classifier: 0.0629 (0.0634) loss_box_reg: 0.1498 (0.1432) loss_objectness: 0.0011 (0.0019) loss_rpn_box_reg: 0.0051 (0.0069) time: 1.0301 data: 0.0222 max mem: 3875 Epoch: [13] [ 30/104] eta: 0:01:17 lr: 0.000001 loss: 0.2237 (0.2180) loss_classifier: 0.0627 (0.0624) loss_box_reg: 0.1510 (0.1468) loss_objectness: 0.0017 (0.0020) loss_rpn_box_reg: 0.0073 (0.0068) time: 1.0236 data: 0.0205 max mem: 3875 Epoch: [13] [ 40/104] eta: 0:01:06 lr: 0.000001 loss: 0.2167 (0.2199) loss_classifier: 0.0592 (0.0630) loss_box_reg: 0.1440 (0.1485) loss_objectness: 0.0014 (0.0018) loss_rpn_box_reg: 0.0050 (0.0066) time: 1.0062 data: 0.0216 max mem: 3875 Epoch: [13] [ 50/104] eta: 0:00:55 lr: 0.000001 loss: 0.2058 (0.2169) loss_classifier: 0.0584 (0.0628) loss_box_reg: 0.1418 (0.1458) loss_objectness: 0.0010 (0.0018) loss_rpn_box_reg: 0.0050 (0.0064) time: 0.9935 data: 0.0228 max mem: 3875 Epoch: [13] [ 60/104] eta: 0:00:44 lr: 0.000001 loss: 0.1800 (0.2084) loss_classifier: 0.0458 (0.0600) loss_box_reg: 0.1195 (0.1408) loss_objectness: 0.0009 (0.0017) loss_rpn_box_reg: 0.0039 (0.0060) time: 0.9843 data: 0.0220 max mem: 3875 Epoch: [13] [ 70/104] eta: 0:00:34 lr: 0.000001 loss: 0.1557 (0.2049) loss_classifier: 0.0400 (0.0591) loss_box_reg: 0.1138 (0.1381) loss_objectness: 0.0008 (0.0018) loss_rpn_box_reg: 0.0031 (0.0059) time: 0.9763 data: 0.0208 max mem: 3875 Epoch: [13] [ 80/104] eta: 0:00:24 lr: 0.000001 loss: 0.1650 (0.2048) loss_classifier: 0.0450 (0.0581) loss_box_reg: 0.1137 (0.1389) loss_objectness: 0.0016 (0.0019) loss_rpn_box_reg: 0.0032 (0.0059) time: 0.9799 data: 0.0215 max mem: 3875 Epoch: [13] [ 90/104] eta: 0:00:14 lr: 0.000001 loss: 0.1724 (0.2060) loss_classifier: 0.0468 (0.0580) loss_box_reg: 0.1282 (0.1401) loss_objectness: 0.0014 (0.0020) loss_rpn_box_reg: 0.0035 (0.0059) time: 0.9912 data: 0.0218 max mem: 3875 Epoch: [13] [100/104] eta: 0:00:04 lr: 0.000001 loss: 0.2136 (0.2077) loss_classifier: 0.0553 (0.0585) loss_box_reg: 0.1544 (0.1414) loss_objectness: 0.0011 (0.0019) loss_rpn_box_reg: 0.0049 (0.0058) time: 0.9947 data: 0.0200 max mem: 3875 Epoch: [13] [103/104] eta: 0:00:01 lr: 0.000001 loss: 0.2011 (0.2070) loss_classifier: 0.0550 (0.0583) loss_box_reg: 0.1402 (0.1410) loss_objectness: 0.0011 (0.0019) loss_rpn_box_reg: 0.0049 (0.0058) time: 0.9969 data: 0.0197 max mem: 3875 Epoch: [13] Total time: 0:01:44 (1.0073 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:39 model_time: 0.4654 (0.4654) evaluator_time: 0.0623 (0.0623) time: 1.5137 data: 0.9798 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4157 (0.4178) evaluator_time: 0.0113 (0.0240) time: 0.4648 data: 0.0185 max mem: 3875 Test: Total time: 0:00:13 (0.5070 s / it) Averaged stats: model_time: 0.4157 (0.4178) evaluator_time: 0.0113 (0.0240) Accumulating evaluation results... DONE (t=0.07s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.549 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.928 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.585 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.487 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.547 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.627 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.236 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.615 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.548 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Epoch: [14] [ 0/104] eta: 0:02:57 lr: 0.000001 loss: 0.1479 (0.1479) loss_classifier: 0.0408 (0.0408) loss_box_reg: 0.1007 (0.1007) loss_objectness: 0.0017 (0.0017) loss_rpn_box_reg: 0.0047 (0.0047) time: 1.7073 data: 0.5621 max mem: 3875 Epoch: [14] [ 10/104] eta: 0:01:41 lr: 0.000001 loss: 0.2075 (0.2239) loss_classifier: 0.0596 (0.0654) loss_box_reg: 0.1381 (0.1493) loss_objectness: 0.0017 (0.0026) loss_rpn_box_reg: 0.0062 (0.0066) time: 1.0822 data: 0.0692 max mem: 3875 Epoch: [14] [ 20/104] eta: 0:01:28 lr: 0.000001 loss: 0.1953 (0.2120) loss_classifier: 0.0568 (0.0602) loss_box_reg: 0.1373 (0.1434) loss_objectness: 0.0013 (0.0022) loss_rpn_box_reg: 0.0049 (0.0062) time: 1.0247 data: 0.0204 max mem: 3875 Epoch: [14] [ 30/104] eta: 0:01:17 lr: 0.000001 loss: 0.1721 (0.1999) loss_classifier: 0.0488 (0.0562) loss_box_reg: 0.1191 (0.1355) loss_objectness: 0.0013 (0.0021) loss_rpn_box_reg: 0.0049 (0.0061) time: 1.0243 data: 0.0215 max mem: 3875 Epoch: [14] [ 40/104] eta: 0:01:06 lr: 0.000001 loss: 0.2000 (0.2106) loss_classifier: 0.0506 (0.0585) loss_box_reg: 0.1414 (0.1435) loss_objectness: 0.0014 (0.0021) loss_rpn_box_reg: 0.0059 (0.0065) time: 1.0097 data: 0.0222 max mem: 3875 Epoch: [14] [ 50/104] eta: 0:00:55 lr: 0.000001 loss: 0.2065 (0.2073) loss_classifier: 0.0590 (0.0581) loss_box_reg: 0.1524 (0.1412) loss_objectness: 0.0014 (0.0019) loss_rpn_box_reg: 0.0051 (0.0061) time: 0.9892 data: 0.0216 max mem: 3875 Epoch: [14] [ 60/104] eta: 0:00:44 lr: 0.000001 loss: 0.1814 (0.2034) loss_classifier: 0.0509 (0.0570) loss_box_reg: 0.1188 (0.1389) loss_objectness: 0.0006 (0.0019) loss_rpn_box_reg: 0.0037 (0.0057) time: 0.9771 data: 0.0212 max mem: 3875 Epoch: [14] [ 70/104] eta: 0:00:34 lr: 0.000001 loss: 0.1878 (0.2071) loss_classifier: 0.0516 (0.0582) loss_box_reg: 0.1317 (0.1411) loss_objectness: 0.0015 (0.0019) loss_rpn_box_reg: 0.0042 (0.0059) time: 0.9815 data: 0.0226 max mem: 3875 Epoch: [14] [ 80/104] eta: 0:00:24 lr: 0.000001 loss: 0.1900 (0.2046) loss_classifier: 0.0562 (0.0580) loss_box_reg: 0.1317 (0.1390) loss_objectness: 0.0015 (0.0019) loss_rpn_box_reg: 0.0052 (0.0057) time: 0.9864 data: 0.0240 max mem: 3875 Epoch: [14] [ 90/104] eta: 0:00:14 lr: 0.000001 loss: 0.1885 (0.2033) loss_classifier: 0.0524 (0.0573) loss_box_reg: 0.1268 (0.1385) loss_objectness: 0.0014 (0.0019) loss_rpn_box_reg: 0.0040 (0.0056) time: 0.9856 data: 0.0220 max mem: 3875 Epoch: [14] [100/104] eta: 0:00:04 lr: 0.000001 loss: 0.1979 (0.2062) loss_classifier: 0.0538 (0.0581) loss_box_reg: 0.1322 (0.1403) loss_objectness: 0.0014 (0.0019) loss_rpn_box_reg: 0.0053 (0.0059) time: 0.9956 data: 0.0202 max mem: 3875 Epoch: [14] [103/104] eta: 0:00:01 lr: 0.000001 loss: 0.2031 (0.2073) loss_classifier: 0.0576 (0.0582) loss_box_reg: 0.1392 (0.1414) loss_objectness: 0.0015 (0.0019) loss_rpn_box_reg: 0.0056 (0.0058) time: 0.9960 data: 0.0196 max mem: 3875 Epoch: [14] Total time: 0:01:44 (1.0063 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:46 model_time: 0.5431 (0.5431) evaluator_time: 0.2347 (0.2347) time: 1.8017 data: 0.9980 max mem: 3875 Test: [25/26] eta: 0:00:00 model_time: 0.4153 (0.4221) evaluator_time: 0.0111 (0.0321) time: 0.4574 data: 0.0175 max mem: 3875 Test: Total time: 0:00:13 (0.5213 s / it) Averaged stats: model_time: 0.4153 (0.4221) evaluator_time: 0.0111 (0.0321) Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.549 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.928 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.585 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.487 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.547 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.627 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.236 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.615 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.548 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673
# to train on gpu if selected.
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
num_classes = 11
# get the model using our helper function
model = get_object_detection_model(num_classes)
# move model to the right device
model.to(device)
# construct an optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.005,
momentum=0.9, weight_decay=0.0005)
# and a learning rate scheduler which decreases the learning rate by
# 10x every 3 epochs
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=3,
gamma=0.1)
# training for 10 epochs
num_epochs = 20
for epoch in range(num_epochs):
# training for one epoch
train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
# update the learning rate
lr_scheduler.step()
# evaluate on the test dataset
evaluate(model, data_loader_test, device=device)
Epoch: [0] [ 0/104] eta: 0:11:35 lr: 0.000053 loss: 2.7235 (2.7235) loss_classifier: 2.3228 (2.3228) loss_box_reg: 0.2357 (0.2357) loss_objectness: 0.1499 (0.1499) loss_rpn_box_reg: 0.0151 (0.0151) time: 6.6916 data: 4.5670 max mem: 4198 Epoch: [0] [ 10/104] eta: 0:02:26 lr: 0.000538 loss: 2.5347 (2.2935) loss_classifier: 1.8309 (1.6350) loss_box_reg: 0.3244 (0.3192) loss_objectness: 0.2873 (0.3125) loss_rpn_box_reg: 0.0241 (0.0267) time: 1.5552 data: 0.4836 max mem: 4355 Epoch: [0] [ 20/104] eta: 0:01:51 lr: 0.001023 loss: 1.2679 (1.6503) loss_classifier: 0.7718 (1.1099) loss_box_reg: 0.2971 (0.3030) loss_objectness: 0.1352 (0.2163) loss_rpn_box_reg: 0.0162 (0.0212) time: 1.0557 data: 0.0636 max mem: 4355 Epoch: [0] [ 30/104] eta: 0:01:30 lr: 0.001508 loss: 0.9645 (1.4740) loss_classifier: 0.4899 (0.9395) loss_box_reg: 0.3449 (0.3378) loss_objectness: 0.0906 (0.1763) loss_rpn_box_reg: 0.0142 (0.0204) time: 1.0487 data: 0.0617 max mem: 4355 Epoch: [0] [ 40/104] eta: 0:01:17 lr: 0.001993 loss: 0.9903 (1.3530) loss_classifier: 0.4853 (0.8226) loss_box_reg: 0.4206 (0.3615) loss_objectness: 0.0740 (0.1492) loss_rpn_box_reg: 0.0139 (0.0198) time: 1.0871 data: 0.1443 max mem: 4355 Epoch: [0] [ 50/104] eta: 0:01:02 lr: 0.002478 loss: 0.9366 (1.2888) loss_classifier: 0.4267 (0.7586) loss_box_reg: 0.4388 (0.3795) loss_objectness: 0.0425 (0.1312) loss_rpn_box_reg: 0.0131 (0.0196) time: 1.0690 data: 0.1398 max mem: 4355 Epoch: [0] [ 60/104] eta: 0:00:50 lr: 0.002963 loss: 0.8294 (1.2162) loss_classifier: 0.3904 (0.6984) loss_box_reg: 0.3764 (0.3836) loss_objectness: 0.0404 (0.1156) loss_rpn_box_reg: 0.0120 (0.0186) time: 1.0098 data: 0.0847 max mem: 4355 Epoch: [0] [ 70/104] eta: 0:00:39 lr: 0.003448 loss: 0.7882 (1.1589) loss_classifier: 0.3670 (0.6539) loss_box_reg: 0.3694 (0.3828) loss_objectness: 0.0283 (0.1033) loss_rpn_box_reg: 0.0120 (0.0189) time: 1.1152 data: 0.1922 max mem: 4355 Epoch: [0] [ 80/104] eta: 0:00:27 lr: 0.003933 loss: 0.8186 (1.1105) loss_classifier: 0.3795 (0.6147) loss_box_reg: 0.3904 (0.3820) loss_objectness: 0.0218 (0.0949) loss_rpn_box_reg: 0.0144 (0.0189) time: 1.1340 data: 0.2052 max mem: 4355 Epoch: [0] [ 90/104] eta: 0:00:15 lr: 0.004418 loss: 0.7866 (1.0753) loss_classifier: 0.3404 (0.5858) loss_box_reg: 0.3858 (0.3820) loss_objectness: 0.0246 (0.0889) loss_rpn_box_reg: 0.0124 (0.0186) time: 1.1019 data: 0.1557 max mem: 4355 Epoch: [0] [100/104] eta: 0:00:04 lr: 0.004903 loss: 0.7400 (1.0362) loss_classifier: 0.2854 (0.5545) loss_box_reg: 0.3585 (0.3797) loss_objectness: 0.0327 (0.0834) loss_rpn_box_reg: 0.0130 (0.0185) time: 1.2998 data: 0.3455 max mem: 4355 Epoch: [0] [103/104] eta: 0:00:01 lr: 0.005000 loss: 0.7400 (1.0289) loss_classifier: 0.2854 (0.5487) loss_box_reg: 0.3585 (0.3797) loss_objectness: 0.0327 (0.0820) loss_rpn_box_reg: 0.0131 (0.0185) time: 1.2825 data: 0.3306 max mem: 4355 Epoch: [0] Total time: 0:02:01 (1.1669 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.4620 (0.4620) evaluator_time: 0.0388 (0.0388) time: 1.2388 data: 0.7165 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4043 (0.4065) evaluator_time: 0.0297 (0.0362) time: 0.4647 data: 0.0182 max mem: 4355 Test: Total time: 0:00:12 (0.4976 s / it) Averaged stats: model_time: 0.4043 (0.4065) evaluator_time: 0.0297 (0.0362) Accumulating evaluation results... DONE (t=0.20s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.594 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.160 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.278 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.268 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.118 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.391 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.330 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.386 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.330 Epoch: [1] [ 0/104] eta: 0:03:02 lr: 0.005000 loss: 0.7628 (0.7628) loss_classifier: 0.2548 (0.2548) loss_box_reg: 0.4684 (0.4684) loss_objectness: 0.0259 (0.0259) loss_rpn_box_reg: 0.0136 (0.0136) time: 1.7539 data: 0.7692 max mem: 4355 Epoch: [1] [ 10/104] eta: 0:01:44 lr: 0.005000 loss: 0.5767 (0.5778) loss_classifier: 0.2348 (0.2165) loss_box_reg: 0.3276 (0.3243) loss_objectness: 0.0185 (0.0217) loss_rpn_box_reg: 0.0118 (0.0152) time: 1.1133 data: 0.0867 max mem: 4355 Epoch: [1] [ 20/104] eta: 0:01:29 lr: 0.005000 loss: 0.4675 (0.5555) loss_classifier: 0.1614 (0.2031) loss_box_reg: 0.3077 (0.3187) loss_objectness: 0.0104 (0.0195) loss_rpn_box_reg: 0.0100 (0.0142) time: 1.0367 data: 0.0259 max mem: 4355 Epoch: [1] [ 30/104] eta: 0:01:18 lr: 0.005000 loss: 0.4675 (0.5371) loss_classifier: 0.1614 (0.2008) loss_box_reg: 0.2925 (0.3037) loss_objectness: 0.0117 (0.0190) loss_rpn_box_reg: 0.0091 (0.0137) time: 1.0227 data: 0.0331 max mem: 4355 Epoch: [1] [ 40/104] eta: 0:01:06 lr: 0.005000 loss: 0.5261 (0.5451) loss_classifier: 0.1940 (0.2019) loss_box_reg: 0.2942 (0.3109) loss_objectness: 0.0136 (0.0184) loss_rpn_box_reg: 0.0108 (0.0139) time: 0.9918 data: 0.0268 max mem: 4355 Epoch: [1] [ 50/104] eta: 0:00:54 lr: 0.005000 loss: 0.5389 (0.5465) loss_classifier: 0.1940 (0.1996) loss_box_reg: 0.3290 (0.3142) loss_objectness: 0.0127 (0.0181) loss_rpn_box_reg: 0.0151 (0.0147) time: 0.9576 data: 0.0213 max mem: 4355 Epoch: [1] [ 60/104] eta: 0:00:44 lr: 0.005000 loss: 0.4767 (0.5370) loss_classifier: 0.1476 (0.1920) loss_box_reg: 0.2888 (0.3127) loss_objectness: 0.0125 (0.0177) loss_rpn_box_reg: 0.0153 (0.0146) time: 0.9472 data: 0.0210 max mem: 4355 Epoch: [1] [ 70/104] eta: 0:00:33 lr: 0.005000 loss: 0.4507 (0.5306) loss_classifier: 0.1376 (0.1887) loss_box_reg: 0.2775 (0.3097) loss_objectness: 0.0130 (0.0179) loss_rpn_box_reg: 0.0121 (0.0143) time: 0.9431 data: 0.0207 max mem: 4355 Epoch: [1] [ 80/104] eta: 0:00:23 lr: 0.005000 loss: 0.4959 (0.5238) loss_classifier: 0.1621 (0.1862) loss_box_reg: 0.3003 (0.3065) loss_objectness: 0.0094 (0.0173) loss_rpn_box_reg: 0.0118 (0.0138) time: 0.9468 data: 0.0213 max mem: 4355 Epoch: [1] [ 90/104] eta: 0:00:13 lr: 0.005000 loss: 0.4317 (0.5092) loss_classifier: 0.1319 (0.1791) loss_box_reg: 0.2675 (0.2997) loss_objectness: 0.0085 (0.0166) loss_rpn_box_reg: 0.0118 (0.0137) time: 0.9522 data: 0.0212 max mem: 4355 Epoch: [1] [100/104] eta: 0:00:03 lr: 0.005000 loss: 0.3731 (0.4952) loss_classifier: 0.1139 (0.1720) loss_box_reg: 0.2457 (0.2939) loss_objectness: 0.0069 (0.0158) loss_rpn_box_reg: 0.0106 (0.0135) time: 0.9506 data: 0.0192 max mem: 4355 Epoch: [1] [103/104] eta: 0:00:00 lr: 0.005000 loss: 0.3872 (0.4931) loss_classifier: 0.1150 (0.1705) loss_box_reg: 0.2577 (0.2935) loss_objectness: 0.0065 (0.0158) loss_rpn_box_reg: 0.0086 (0.0133) time: 0.9518 data: 0.0192 max mem: 4355 Epoch: [1] Total time: 0:01:42 (0.9831 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:41 model_time: 0.5403 (0.5403) evaluator_time: 0.0770 (0.0770) time: 1.6116 data: 0.9682 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4011 (0.4075) evaluator_time: 0.0145 (0.0261) time: 0.4491 data: 0.0177 max mem: 4355 Test: Total time: 0:00:13 (0.5019 s / it) Averaged stats: model_time: 0.4011 (0.4075) evaluator_time: 0.0145 (0.0261) Accumulating evaluation results... DONE (t=0.23s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.435 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.840 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.384 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.347 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.425 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.361 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.183 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.453 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.522 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.465 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.509 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.494 Epoch: [2] [ 0/104] eta: 0:03:53 lr: 0.005000 loss: 0.4320 (0.4320) loss_classifier: 0.1283 (0.1283) loss_box_reg: 0.2721 (0.2721) loss_objectness: 0.0149 (0.0149) loss_rpn_box_reg: 0.0168 (0.0168) time: 2.2433 data: 1.1228 max mem: 4355 Epoch: [2] [ 10/104] eta: 0:01:41 lr: 0.005000 loss: 0.4269 (0.3874) loss_classifier: 0.1283 (0.1159) loss_box_reg: 0.2525 (0.2509) loss_objectness: 0.0081 (0.0098) loss_rpn_box_reg: 0.0102 (0.0107) time: 1.0842 data: 0.1168 max mem: 4355 Epoch: [2] [ 20/104] eta: 0:01:27 lr: 0.005000 loss: 0.4396 (0.4110) loss_classifier: 0.1152 (0.1137) loss_box_reg: 0.2905 (0.2789) loss_objectness: 0.0060 (0.0083) loss_rpn_box_reg: 0.0094 (0.0100) time: 0.9769 data: 0.0177 max mem: 4355 Epoch: [2] [ 30/104] eta: 0:01:15 lr: 0.005000 loss: 0.4396 (0.3977) loss_classifier: 0.1056 (0.1137) loss_box_reg: 0.2872 (0.2660) loss_objectness: 0.0051 (0.0076) loss_rpn_box_reg: 0.0094 (0.0102) time: 0.9839 data: 0.0207 max mem: 4355 Epoch: [2] [ 40/104] eta: 0:01:04 lr: 0.005000 loss: 0.3588 (0.3833) loss_classifier: 0.1041 (0.1127) loss_box_reg: 0.2210 (0.2532) loss_objectness: 0.0044 (0.0069) loss_rpn_box_reg: 0.0103 (0.0104) time: 0.9737 data: 0.0212 max mem: 4355 Epoch: [2] [ 50/104] eta: 0:00:53 lr: 0.005000 loss: 0.3516 (0.3717) loss_classifier: 0.0992 (0.1090) loss_box_reg: 0.2057 (0.2460) loss_objectness: 0.0034 (0.0064) loss_rpn_box_reg: 0.0103 (0.0103) time: 0.9580 data: 0.0201 max mem: 4355 Epoch: [2] [ 60/104] eta: 0:00:43 lr: 0.005000 loss: 0.3467 (0.3702) loss_classifier: 0.0824 (0.1086) loss_box_reg: 0.2244 (0.2451) loss_objectness: 0.0035 (0.0063) loss_rpn_box_reg: 0.0079 (0.0101) time: 0.9483 data: 0.0194 max mem: 4355 Epoch: [2] [ 70/104] eta: 0:00:33 lr: 0.005000 loss: 0.3447 (0.3655) loss_classifier: 0.0935 (0.1070) loss_box_reg: 0.2187 (0.2422) loss_objectness: 0.0051 (0.0064) loss_rpn_box_reg: 0.0079 (0.0099) time: 0.9445 data: 0.0193 max mem: 4355 Epoch: [2] [ 80/104] eta: 0:00:23 lr: 0.005000 loss: 0.3687 (0.3694) loss_classifier: 0.1029 (0.1079) loss_box_reg: 0.2330 (0.2452) loss_objectness: 0.0051 (0.0061) loss_rpn_box_reg: 0.0077 (0.0102) time: 0.9436 data: 0.0198 max mem: 4355 Epoch: [2] [ 90/104] eta: 0:00:13 lr: 0.005000 loss: 0.3849 (0.3707) loss_classifier: 0.1032 (0.1066) loss_box_reg: 0.2601 (0.2481) loss_objectness: 0.0039 (0.0060) loss_rpn_box_reg: 0.0075 (0.0101) time: 0.9504 data: 0.0211 max mem: 4355 Epoch: [2] [100/104] eta: 0:00:03 lr: 0.005000 loss: 0.3699 (0.3734) loss_classifier: 0.0956 (0.1076) loss_box_reg: 0.2644 (0.2494) loss_objectness: 0.0047 (0.0062) loss_rpn_box_reg: 0.0064 (0.0101) time: 0.9547 data: 0.0202 max mem: 4355 Epoch: [2] [103/104] eta: 0:00:00 lr: 0.005000 loss: 0.3719 (0.3723) loss_classifier: 0.0956 (0.1077) loss_box_reg: 0.2644 (0.2483) loss_objectness: 0.0061 (0.0062) loss_rpn_box_reg: 0.0085 (0.0101) time: 0.9520 data: 0.0186 max mem: 4355 Epoch: [2] Total time: 0:01:41 (0.9723 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:27 model_time: 0.4974 (0.4974) evaluator_time: 0.0442 (0.0442) time: 1.0693 data: 0.5080 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4038 (0.4073) evaluator_time: 0.0153 (0.0251) time: 0.4540 data: 0.0192 max mem: 4355 Test: Total time: 0:00:12 (0.4815 s / it) Averaged stats: model_time: 0.4038 (0.4073) evaluator_time: 0.0153 (0.0251) Accumulating evaluation results... DONE (t=0.11s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.465 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.878 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.438 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.336 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.465 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.374 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.196 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.481 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.549 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.491 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.554 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.591 Epoch: [3] [ 0/104] eta: 0:02:45 lr: 0.000500 loss: 0.3921 (0.3921) loss_classifier: 0.1305 (0.1305) loss_box_reg: 0.2452 (0.2452) loss_objectness: 0.0076 (0.0076) loss_rpn_box_reg: 0.0089 (0.0089) time: 1.5954 data: 0.5706 max mem: 4355 Epoch: [3] [ 10/104] eta: 0:01:36 lr: 0.000500 loss: 0.3498 (0.3337) loss_classifier: 0.0860 (0.0953) loss_box_reg: 0.2140 (0.2222) loss_objectness: 0.0032 (0.0046) loss_rpn_box_reg: 0.0089 (0.0117) time: 1.0275 data: 0.0692 max mem: 4355 Epoch: [3] [ 20/104] eta: 0:01:24 lr: 0.000500 loss: 0.2819 (0.2992) loss_classifier: 0.0810 (0.0836) loss_box_reg: 0.1910 (0.2023) loss_objectness: 0.0030 (0.0040) loss_rpn_box_reg: 0.0067 (0.0092) time: 0.9759 data: 0.0190 max mem: 4355 Epoch: [3] [ 30/104] eta: 0:01:13 lr: 0.000500 loss: 0.2535 (0.3029) loss_classifier: 0.0745 (0.0833) loss_box_reg: 0.1924 (0.2066) loss_objectness: 0.0030 (0.0042) loss_rpn_box_reg: 0.0057 (0.0088) time: 0.9776 data: 0.0192 max mem: 4355 Epoch: [3] [ 40/104] eta: 0:01:03 lr: 0.000500 loss: 0.2572 (0.2946) loss_classifier: 0.0745 (0.0822) loss_box_reg: 0.1924 (0.1994) loss_objectness: 0.0036 (0.0043) loss_rpn_box_reg: 0.0075 (0.0086) time: 0.9670 data: 0.0197 max mem: 4355 Epoch: [3] [ 50/104] eta: 0:00:52 lr: 0.000500 loss: 0.2402 (0.2868) loss_classifier: 0.0735 (0.0808) loss_box_reg: 0.1591 (0.1937) loss_objectness: 0.0027 (0.0041) loss_rpn_box_reg: 0.0062 (0.0083) time: 0.9513 data: 0.0189 max mem: 4355 Epoch: [3] [ 60/104] eta: 0:00:42 lr: 0.000500 loss: 0.2573 (0.2853) loss_classifier: 0.0810 (0.0809) loss_box_reg: 0.1642 (0.1923) loss_objectness: 0.0024 (0.0039) loss_rpn_box_reg: 0.0059 (0.0082) time: 0.9440 data: 0.0187 max mem: 4355 Epoch: [3] [ 70/104] eta: 0:00:32 lr: 0.000500 loss: 0.2573 (0.2814) loss_classifier: 0.0799 (0.0798) loss_box_reg: 0.1760 (0.1898) loss_objectness: 0.0023 (0.0037) loss_rpn_box_reg: 0.0053 (0.0080) time: 0.9472 data: 0.0211 max mem: 4355 Epoch: [3] [ 80/104] eta: 0:00:23 lr: 0.000500 loss: 0.2383 (0.2781) loss_classifier: 0.0648 (0.0788) loss_box_reg: 0.1659 (0.1874) loss_objectness: 0.0023 (0.0036) loss_rpn_box_reg: 0.0047 (0.0083) time: 0.9517 data: 0.0217 max mem: 4355 Epoch: [3] [ 90/104] eta: 0:00:13 lr: 0.000500 loss: 0.2320 (0.2716) loss_classifier: 0.0633 (0.0769) loss_box_reg: 0.1460 (0.1835) loss_objectness: 0.0011 (0.0033) loss_rpn_box_reg: 0.0047 (0.0079) time: 0.9578 data: 0.0221 max mem: 4355 Epoch: [3] [100/104] eta: 0:00:03 lr: 0.000500 loss: 0.2102 (0.2685) loss_classifier: 0.0623 (0.0763) loss_box_reg: 0.1523 (0.1811) loss_objectness: 0.0012 (0.0032) loss_rpn_box_reg: 0.0048 (0.0079) time: 0.9575 data: 0.0216 max mem: 4355 Epoch: [3] [103/104] eta: 0:00:00 lr: 0.000500 loss: 0.2446 (0.2679) loss_classifier: 0.0623 (0.0763) loss_box_reg: 0.1544 (0.1807) loss_objectness: 0.0012 (0.0032) loss_rpn_box_reg: 0.0048 (0.0078) time: 0.9498 data: 0.0192 max mem: 4355 Epoch: [3] Total time: 0:01:40 (0.9662 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:30 model_time: 0.5140 (0.5140) evaluator_time: 0.0180 (0.0180) time: 1.1602 data: 0.6156 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4073 (0.4099) evaluator_time: 0.0152 (0.0249) time: 0.4665 data: 0.0246 max mem: 4355 Test: Total time: 0:00:12 (0.4921 s / it) Averaged stats: model_time: 0.4073 (0.4099) evaluator_time: 0.0152 (0.0249) Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.530 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.887 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.576 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.359 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.532 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.468 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.422 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.599 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.515 Epoch: [4] [ 0/104] eta: 0:02:48 lr: 0.000500 loss: 0.1289 (0.1289) loss_classifier: 0.0386 (0.0386) loss_box_reg: 0.0862 (0.0862) loss_objectness: 0.0019 (0.0019) loss_rpn_box_reg: 0.0022 (0.0022) time: 1.6187 data: 0.5553 max mem: 4355 Epoch: [4] [ 10/104] eta: 0:01:37 lr: 0.000500 loss: 0.2817 (0.2439) loss_classifier: 0.0818 (0.0724) loss_box_reg: 0.1687 (0.1613) loss_objectness: 0.0019 (0.0030) loss_rpn_box_reg: 0.0061 (0.0072) time: 1.0355 data: 0.0702 max mem: 4355 Epoch: [4] [ 20/104] eta: 0:01:24 lr: 0.000500 loss: 0.2388 (0.2390) loss_classifier: 0.0657 (0.0695) loss_box_reg: 0.1585 (0.1599) loss_objectness: 0.0019 (0.0031) loss_rpn_box_reg: 0.0061 (0.0066) time: 0.9768 data: 0.0204 max mem: 4355 Epoch: [4] [ 30/104] eta: 0:01:13 lr: 0.000500 loss: 0.2285 (0.2361) loss_classifier: 0.0645 (0.0672) loss_box_reg: 0.1585 (0.1598) loss_objectness: 0.0020 (0.0028) loss_rpn_box_reg: 0.0052 (0.0063) time: 0.9769 data: 0.0205 max mem: 4355 Epoch: [4] [ 40/104] eta: 0:01:03 lr: 0.000500 loss: 0.2079 (0.2274) loss_classifier: 0.0566 (0.0645) loss_box_reg: 0.1348 (0.1541) loss_objectness: 0.0016 (0.0026) loss_rpn_box_reg: 0.0038 (0.0062) time: 0.9729 data: 0.0222 max mem: 4355 Epoch: [4] [ 50/104] eta: 0:00:53 lr: 0.000500 loss: 0.2170 (0.2308) loss_classifier: 0.0616 (0.0657) loss_box_reg: 0.1520 (0.1558) loss_objectness: 0.0017 (0.0025) loss_rpn_box_reg: 0.0043 (0.0068) time: 0.9586 data: 0.0205 max mem: 4355 Epoch: [4] [ 60/104] eta: 0:00:42 lr: 0.000500 loss: 0.2221 (0.2337) loss_classifier: 0.0697 (0.0672) loss_box_reg: 0.1547 (0.1569) loss_objectness: 0.0021 (0.0026) loss_rpn_box_reg: 0.0051 (0.0071) time: 0.9470 data: 0.0189 max mem: 4355 Epoch: [4] [ 70/104] eta: 0:00:33 lr: 0.000500 loss: 0.2253 (0.2370) loss_classifier: 0.0692 (0.0679) loss_box_reg: 0.1447 (0.1593) loss_objectness: 0.0023 (0.0026) loss_rpn_box_reg: 0.0056 (0.0072) time: 0.9469 data: 0.0213 max mem: 4355 Epoch: [4] [ 80/104] eta: 0:00:23 lr: 0.000500 loss: 0.2193 (0.2386) loss_classifier: 0.0669 (0.0676) loss_box_reg: 0.1486 (0.1612) loss_objectness: 0.0019 (0.0026) loss_rpn_box_reg: 0.0056 (0.0072) time: 0.9489 data: 0.0220 max mem: 4355 Epoch: [4] [ 90/104] eta: 0:00:13 lr: 0.000500 loss: 0.2061 (0.2377) loss_classifier: 0.0658 (0.0671) loss_box_reg: 0.1486 (0.1609) loss_objectness: 0.0014 (0.0026) loss_rpn_box_reg: 0.0054 (0.0071) time: 0.9513 data: 0.0213 max mem: 4355 Epoch: [4] [100/104] eta: 0:00:03 lr: 0.000500 loss: 0.2612 (0.2428) loss_classifier: 0.0776 (0.0687) loss_box_reg: 0.1740 (0.1644) loss_objectness: 0.0019 (0.0026) loss_rpn_box_reg: 0.0062 (0.0072) time: 0.9562 data: 0.0201 max mem: 4355 Epoch: [4] [103/104] eta: 0:00:00 lr: 0.000500 loss: 0.2450 (0.2410) loss_classifier: 0.0685 (0.0682) loss_box_reg: 0.1671 (0.1631) loss_objectness: 0.0019 (0.0026) loss_rpn_box_reg: 0.0052 (0.0071) time: 0.9538 data: 0.0189 max mem: 4355 Epoch: [4] Total time: 0:01:40 (0.9673 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:34 model_time: 0.4584 (0.4584) evaluator_time: 0.0162 (0.0162) time: 1.3111 data: 0.8223 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4042 (0.4074) evaluator_time: 0.0125 (0.0184) time: 0.4521 data: 0.0208 max mem: 4355 Test: Total time: 0:00:12 (0.4885 s / it) Averaged stats: model_time: 0.4042 (0.4074) evaluator_time: 0.0125 (0.0184) Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.534 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.887 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.600 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.531 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.464 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.526 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.427 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.598 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.497 Epoch: [5] [ 0/104] eta: 0:02:52 lr: 0.000500 loss: 0.2231 (0.2231) loss_classifier: 0.0715 (0.0715) loss_box_reg: 0.1476 (0.1476) loss_objectness: 0.0005 (0.0005) loss_rpn_box_reg: 0.0035 (0.0035) time: 1.6560 data: 0.6650 max mem: 4355 Epoch: [5] [ 10/104] eta: 0:01:37 lr: 0.000500 loss: 0.2231 (0.2124) loss_classifier: 0.0647 (0.0623) loss_box_reg: 0.1483 (0.1440) loss_objectness: 0.0017 (0.0016) loss_rpn_box_reg: 0.0043 (0.0046) time: 1.0365 data: 0.0794 max mem: 4355 Epoch: [5] [ 20/104] eta: 0:01:25 lr: 0.000500 loss: 0.2201 (0.2191) loss_classifier: 0.0637 (0.0628) loss_box_reg: 0.1483 (0.1480) loss_objectness: 0.0018 (0.0020) loss_rpn_box_reg: 0.0050 (0.0063) time: 0.9815 data: 0.0213 max mem: 4355 Epoch: [5] [ 30/104] eta: 0:01:13 lr: 0.000500 loss: 0.2193 (0.2201) loss_classifier: 0.0611 (0.0623) loss_box_reg: 0.1514 (0.1493) loss_objectness: 0.0018 (0.0021) loss_rpn_box_reg: 0.0065 (0.0064) time: 0.9794 data: 0.0212 max mem: 4355 Epoch: [5] [ 40/104] eta: 0:01:03 lr: 0.000500 loss: 0.2193 (0.2213) loss_classifier: 0.0586 (0.0630) loss_box_reg: 0.1522 (0.1496) loss_objectness: 0.0013 (0.0020) loss_rpn_box_reg: 0.0053 (0.0067) time: 0.9685 data: 0.0215 max mem: 4355 Epoch: [5] [ 50/104] eta: 0:00:53 lr: 0.000500 loss: 0.2117 (0.2212) loss_classifier: 0.0611 (0.0635) loss_box_reg: 0.1343 (0.1490) loss_objectness: 0.0014 (0.0019) loss_rpn_box_reg: 0.0045 (0.0068) time: 0.9616 data: 0.0218 max mem: 4355 Epoch: [5] [ 60/104] eta: 0:00:43 lr: 0.000500 loss: 0.2117 (0.2223) loss_classifier: 0.0637 (0.0641) loss_box_reg: 0.1343 (0.1496) loss_objectness: 0.0016 (0.0020) loss_rpn_box_reg: 0.0063 (0.0066) time: 0.9495 data: 0.0211 max mem: 4355 Epoch: [5] [ 70/104] eta: 0:00:33 lr: 0.000500 loss: 0.2423 (0.2266) loss_classifier: 0.0665 (0.0650) loss_box_reg: 0.1721 (0.1528) loss_objectness: 0.0026 (0.0022) loss_rpn_box_reg: 0.0067 (0.0067) time: 0.9458 data: 0.0211 max mem: 4355 Epoch: [5] [ 80/104] eta: 0:00:23 lr: 0.000500 loss: 0.2581 (0.2314) loss_classifier: 0.0672 (0.0657) loss_box_reg: 0.1741 (0.1566) loss_objectness: 0.0023 (0.0021) loss_rpn_box_reg: 0.0065 (0.0070) time: 0.9506 data: 0.0222 max mem: 4355 Epoch: [5] [ 90/104] eta: 0:00:13 lr: 0.000500 loss: 0.2467 (0.2312) loss_classifier: 0.0638 (0.0654) loss_box_reg: 0.1741 (0.1568) loss_objectness: 0.0016 (0.0021) loss_rpn_box_reg: 0.0052 (0.0068) time: 0.9534 data: 0.0226 max mem: 4355 Epoch: [5] [100/104] eta: 0:00:03 lr: 0.000500 loss: 0.2264 (0.2324) loss_classifier: 0.0638 (0.0659) loss_box_reg: 0.1560 (0.1573) loss_objectness: 0.0016 (0.0022) loss_rpn_box_reg: 0.0061 (0.0069) time: 0.9632 data: 0.0234 max mem: 4355 Epoch: [5] [103/104] eta: 0:00:00 lr: 0.000500 loss: 0.2264 (0.2302) loss_classifier: 0.0638 (0.0653) loss_box_reg: 0.1560 (0.1559) loss_objectness: 0.0017 (0.0022) loss_rpn_box_reg: 0.0062 (0.0068) time: 0.9634 data: 0.0232 max mem: 4355 Epoch: [5] Total time: 0:01:40 (0.9698 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:31 model_time: 0.4610 (0.4610) evaluator_time: 0.0177 (0.0177) time: 1.2052 data: 0.7138 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4044 (0.4084) evaluator_time: 0.0100 (0.0197) time: 0.4468 data: 0.0189 max mem: 4355 Test: Total time: 0:00:12 (0.4852 s / it) Averaged stats: model_time: 0.4044 (0.4084) evaluator_time: 0.0100 (0.0197) Accumulating evaluation results... DONE (t=0.07s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.530 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.893 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.580 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.366 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.532 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.460 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.223 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.529 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.429 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.604 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.497 Epoch: [6] [ 0/104] eta: 0:03:02 lr: 0.000050 loss: 0.2072 (0.2072) loss_classifier: 0.0535 (0.0535) loss_box_reg: 0.1461 (0.1461) loss_objectness: 0.0027 (0.0027) loss_rpn_box_reg: 0.0049 (0.0049) time: 1.7548 data: 0.7822 max mem: 4355 Epoch: [6] [ 10/104] eta: 0:01:37 lr: 0.000050 loss: 0.2284 (0.2237) loss_classifier: 0.0589 (0.0612) loss_box_reg: 0.1529 (0.1548) loss_objectness: 0.0017 (0.0018) loss_rpn_box_reg: 0.0059 (0.0059) time: 1.0415 data: 0.0879 max mem: 4355 Epoch: [6] [ 20/104] eta: 0:01:25 lr: 0.000050 loss: 0.2352 (0.2425) loss_classifier: 0.0629 (0.0678) loss_box_reg: 0.1653 (0.1652) loss_objectness: 0.0017 (0.0022) loss_rpn_box_reg: 0.0068 (0.0073) time: 0.9772 data: 0.0194 max mem: 4355 Epoch: [6] [ 30/104] eta: 0:01:13 lr: 0.000050 loss: 0.2539 (0.2399) loss_classifier: 0.0629 (0.0681) loss_box_reg: 0.1596 (0.1623) loss_objectness: 0.0014 (0.0021) loss_rpn_box_reg: 0.0093 (0.0074) time: 0.9755 data: 0.0197 max mem: 4355 Epoch: [6] [ 40/104] eta: 0:01:03 lr: 0.000050 loss: 0.2085 (0.2329) loss_classifier: 0.0577 (0.0660) loss_box_reg: 0.1449 (0.1580) loss_objectness: 0.0011 (0.0020) loss_rpn_box_reg: 0.0043 (0.0069) time: 0.9653 data: 0.0206 max mem: 4355 Epoch: [6] [ 50/104] eta: 0:00:53 lr: 0.000050 loss: 0.2085 (0.2312) loss_classifier: 0.0580 (0.0651) loss_box_reg: 0.1435 (0.1573) loss_objectness: 0.0022 (0.0022) loss_rpn_box_reg: 0.0041 (0.0067) time: 0.9580 data: 0.0208 max mem: 4355 Epoch: [6] [ 60/104] eta: 0:00:42 lr: 0.000050 loss: 0.2050 (0.2286) loss_classifier: 0.0580 (0.0648) loss_box_reg: 0.1381 (0.1549) loss_objectness: 0.0019 (0.0023) loss_rpn_box_reg: 0.0043 (0.0066) time: 0.9488 data: 0.0201 max mem: 4355 Epoch: [6] [ 70/104] eta: 0:00:33 lr: 0.000050 loss: 0.2039 (0.2241) loss_classifier: 0.0560 (0.0632) loss_box_reg: 0.1381 (0.1522) loss_objectness: 0.0018 (0.0023) loss_rpn_box_reg: 0.0043 (0.0064) time: 0.9452 data: 0.0215 max mem: 4355 Epoch: [6] [ 80/104] eta: 0:00:23 lr: 0.000050 loss: 0.2100 (0.2229) loss_classifier: 0.0583 (0.0633) loss_box_reg: 0.1404 (0.1511) loss_objectness: 0.0018 (0.0023) loss_rpn_box_reg: 0.0044 (0.0063) time: 0.9473 data: 0.0207 max mem: 4355 Epoch: [6] [ 90/104] eta: 0:00:13 lr: 0.000050 loss: 0.2112 (0.2228) loss_classifier: 0.0583 (0.0631) loss_box_reg: 0.1423 (0.1507) loss_objectness: 0.0018 (0.0023) loss_rpn_box_reg: 0.0045 (0.0067) time: 0.9526 data: 0.0200 max mem: 4355 Epoch: [6] [100/104] eta: 0:00:03 lr: 0.000050 loss: 0.1688 (0.2184) loss_classifier: 0.0461 (0.0620) loss_box_reg: 0.1179 (0.1478) loss_objectness: 0.0013 (0.0022) loss_rpn_box_reg: 0.0037 (0.0065) time: 0.9493 data: 0.0192 max mem: 4355 Epoch: [6] [103/104] eta: 0:00:00 lr: 0.000050 loss: 0.1812 (0.2190) loss_classifier: 0.0516 (0.0623) loss_box_reg: 0.1246 (0.1480) loss_objectness: 0.0013 (0.0022) loss_rpn_box_reg: 0.0043 (0.0065) time: 0.9515 data: 0.0192 max mem: 4355 Epoch: [6] Total time: 0:01:40 (0.9666 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:43 model_time: 0.5862 (0.5862) evaluator_time: 0.0531 (0.0531) time: 1.6826 data: 1.0199 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4025 (0.4096) evaluator_time: 0.0108 (0.0173) time: 0.4406 data: 0.0175 max mem: 4355 Test: Total time: 0:00:13 (0.5091 s / it) Averaged stats: model_time: 0.4025 (0.4096) evaluator_time: 0.0108 (0.0173) Accumulating evaluation results... DONE (t=0.14s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.894 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.596 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.369 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.542 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.478 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.535 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.610 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.614 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.552 Epoch: [7] [ 0/104] eta: 0:04:01 lr: 0.000050 loss: 0.1735 (0.1735) loss_classifier: 0.0524 (0.0524) loss_box_reg: 0.1117 (0.1117) loss_objectness: 0.0007 (0.0007) loss_rpn_box_reg: 0.0087 (0.0087) time: 2.3236 data: 1.1759 max mem: 4355 Epoch: [7] [ 10/104] eta: 0:01:42 lr: 0.000050 loss: 0.2170 (0.2226) loss_classifier: 0.0638 (0.0633) loss_box_reg: 0.1557 (0.1511) loss_objectness: 0.0025 (0.0021) loss_rpn_box_reg: 0.0053 (0.0061) time: 1.0877 data: 0.1215 max mem: 4355 Epoch: [7] [ 20/104] eta: 0:01:27 lr: 0.000050 loss: 0.2234 (0.2211) loss_classifier: 0.0653 (0.0631) loss_box_reg: 0.1588 (0.1487) loss_objectness: 0.0023 (0.0022) loss_rpn_box_reg: 0.0053 (0.0072) time: 0.9759 data: 0.0198 max mem: 4355 Epoch: [7] [ 30/104] eta: 0:01:15 lr: 0.000050 loss: 0.2398 (0.2309) loss_classifier: 0.0718 (0.0656) loss_box_reg: 0.1588 (0.1551) loss_objectness: 0.0019 (0.0024) loss_rpn_box_reg: 0.0068 (0.0078) time: 0.9786 data: 0.0213 max mem: 4355 Epoch: [7] [ 40/104] eta: 0:01:04 lr: 0.000050 loss: 0.2227 (0.2237) loss_classifier: 0.0631 (0.0640) loss_box_reg: 0.1458 (0.1501) loss_objectness: 0.0015 (0.0023) loss_rpn_box_reg: 0.0052 (0.0073) time: 0.9627 data: 0.0193 max mem: 4355 Epoch: [7] [ 50/104] eta: 0:00:53 lr: 0.000050 loss: 0.2049 (0.2202) loss_classifier: 0.0573 (0.0635) loss_box_reg: 0.1324 (0.1476) loss_objectness: 0.0013 (0.0022) loss_rpn_box_reg: 0.0048 (0.0069) time: 0.9543 data: 0.0199 max mem: 4355 Epoch: [7] [ 60/104] eta: 0:00:43 lr: 0.000050 loss: 0.1857 (0.2151) loss_classifier: 0.0554 (0.0616) loss_box_reg: 0.1299 (0.1448) loss_objectness: 0.0016 (0.0021) loss_rpn_box_reg: 0.0039 (0.0066) time: 0.9568 data: 0.0227 max mem: 4355 Epoch: [7] [ 70/104] eta: 0:00:33 lr: 0.000050 loss: 0.1857 (0.2130) loss_classifier: 0.0501 (0.0611) loss_box_reg: 0.1299 (0.1434) loss_objectness: 0.0009 (0.0020) loss_rpn_box_reg: 0.0044 (0.0065) time: 0.9628 data: 0.0259 max mem: 4355 Epoch: [7] [ 80/104] eta: 0:00:23 lr: 0.000050 loss: 0.2195 (0.2148) loss_classifier: 0.0552 (0.0615) loss_box_reg: 0.1474 (0.1447) loss_objectness: 0.0015 (0.0021) loss_rpn_box_reg: 0.0059 (0.0066) time: 0.9571 data: 0.0234 max mem: 4355 Epoch: [7] [ 90/104] eta: 0:00:13 lr: 0.000050 loss: 0.2476 (0.2177) loss_classifier: 0.0651 (0.0621) loss_box_reg: 0.1683 (0.1469) loss_objectness: 0.0015 (0.0021) loss_rpn_box_reg: 0.0055 (0.0066) time: 0.9523 data: 0.0205 max mem: 4355 Epoch: [7] [100/104] eta: 0:00:03 lr: 0.000050 loss: 0.2052 (0.2143) loss_classifier: 0.0567 (0.0611) loss_box_reg: 0.1451 (0.1447) loss_objectness: 0.0009 (0.0020) loss_rpn_box_reg: 0.0048 (0.0064) time: 0.9548 data: 0.0199 max mem: 4355 Epoch: [7] [103/104] eta: 0:00:00 lr: 0.000050 loss: 0.2052 (0.2158) loss_classifier: 0.0572 (0.0615) loss_box_reg: 0.1451 (0.1459) loss_objectness: 0.0010 (0.0020) loss_rpn_box_reg: 0.0049 (0.0065) time: 0.9557 data: 0.0194 max mem: 4355 Epoch: [7] Total time: 0:01:41 (0.9755 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:29 model_time: 0.4586 (0.4586) evaluator_time: 0.0161 (0.0161) time: 1.1438 data: 0.6522 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4044 (0.4062) evaluator_time: 0.0137 (0.0190) time: 0.4496 data: 0.0196 max mem: 4355 Test: Total time: 0:00:12 (0.4794 s / it) Averaged stats: model_time: 0.4044 (0.4062) evaluator_time: 0.0137 (0.0190) Accumulating evaluation results... DONE (t=0.07s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.539 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.891 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.596 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.601 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577 Epoch: [8] [ 0/104] eta: 0:02:53 lr: 0.000050 loss: 0.1753 (0.1753) loss_classifier: 0.0522 (0.0522) loss_box_reg: 0.1133 (0.1133) loss_objectness: 0.0014 (0.0014) loss_rpn_box_reg: 0.0083 (0.0083) time: 1.6643 data: 0.5835 max mem: 4355 Epoch: [8] [ 10/104] eta: 0:01:37 lr: 0.000050 loss: 0.2261 (0.2278) loss_classifier: 0.0617 (0.0615) loss_box_reg: 0.1555 (0.1558) loss_objectness: 0.0024 (0.0026) loss_rpn_box_reg: 0.0065 (0.0079) time: 1.0320 data: 0.0711 max mem: 4355 Epoch: [8] [ 20/104] eta: 0:01:24 lr: 0.000050 loss: 0.2177 (0.2206) loss_classifier: 0.0589 (0.0616) loss_box_reg: 0.1358 (0.1496) loss_objectness: 0.0020 (0.0023) loss_rpn_box_reg: 0.0059 (0.0071) time: 0.9735 data: 0.0197 max mem: 4355 Epoch: [8] [ 30/104] eta: 0:01:13 lr: 0.000050 loss: 0.1976 (0.2221) loss_classifier: 0.0586 (0.0619) loss_box_reg: 0.1358 (0.1511) loss_objectness: 0.0019 (0.0022) loss_rpn_box_reg: 0.0055 (0.0069) time: 0.9794 data: 0.0218 max mem: 4355 Epoch: [8] [ 40/104] eta: 0:01:03 lr: 0.000050 loss: 0.1850 (0.2166) loss_classifier: 0.0518 (0.0603) loss_box_reg: 0.1283 (0.1468) loss_objectness: 0.0015 (0.0024) loss_rpn_box_reg: 0.0053 (0.0072) time: 0.9735 data: 0.0227 max mem: 4355 Epoch: [8] [ 50/104] eta: 0:00:52 lr: 0.000050 loss: 0.1868 (0.2197) loss_classifier: 0.0577 (0.0623) loss_box_reg: 0.1283 (0.1483) loss_objectness: 0.0008 (0.0021) loss_rpn_box_reg: 0.0043 (0.0070) time: 0.9556 data: 0.0201 max mem: 4355 Epoch: [8] [ 60/104] eta: 0:00:42 lr: 0.000050 loss: 0.1878 (0.2183) loss_classifier: 0.0593 (0.0623) loss_box_reg: 0.1294 (0.1472) loss_objectness: 0.0010 (0.0021) loss_rpn_box_reg: 0.0038 (0.0067) time: 0.9475 data: 0.0201 max mem: 4355 Epoch: [8] [ 70/104] eta: 0:00:33 lr: 0.000050 loss: 0.1817 (0.2130) loss_classifier: 0.0540 (0.0611) loss_box_reg: 0.1209 (0.1434) loss_objectness: 0.0012 (0.0020) loss_rpn_box_reg: 0.0035 (0.0064) time: 0.9480 data: 0.0212 max mem: 4355 Epoch: [8] [ 80/104] eta: 0:00:23 lr: 0.000050 loss: 0.1902 (0.2135) loss_classifier: 0.0536 (0.0612) loss_box_reg: 0.1259 (0.1437) loss_objectness: 0.0015 (0.0020) loss_rpn_box_reg: 0.0059 (0.0067) time: 0.9472 data: 0.0207 max mem: 4355 Epoch: [8] [ 90/104] eta: 0:00:13 lr: 0.000050 loss: 0.2300 (0.2139) loss_classifier: 0.0589 (0.0611) loss_box_reg: 0.1582 (0.1444) loss_objectness: 0.0015 (0.0020) loss_rpn_box_reg: 0.0067 (0.0065) time: 0.9505 data: 0.0206 max mem: 4355 Epoch: [8] [100/104] eta: 0:00:03 lr: 0.000050 loss: 0.2215 (0.2139) loss_classifier: 0.0590 (0.0610) loss_box_reg: 0.1558 (0.1447) loss_objectness: 0.0013 (0.0019) loss_rpn_box_reg: 0.0047 (0.0064) time: 0.9548 data: 0.0209 max mem: 4355 Epoch: [8] [103/104] eta: 0:00:00 lr: 0.000050 loss: 0.2166 (0.2152) loss_classifier: 0.0605 (0.0613) loss_box_reg: 0.1458 (0.1454) loss_objectness: 0.0012 (0.0020) loss_rpn_box_reg: 0.0048 (0.0065) time: 0.9511 data: 0.0196 max mem: 4355 Epoch: [8] Total time: 0:01:40 (0.9669 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.5011 (0.5011) evaluator_time: 0.0222 (0.0222) time: 1.2310 data: 0.6990 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4050 (0.4083) evaluator_time: 0.0148 (0.0189) time: 0.4512 data: 0.0197 max mem: 4355 Test: Total time: 0:00:12 (0.4826 s / it) Averaged stats: model_time: 0.4050 (0.4083) evaluator_time: 0.0148 (0.0189) Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.891 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.599 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.483 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.531 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.432 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.603 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.576 Epoch: [9] [ 0/104] eta: 0:02:59 lr: 0.000005 loss: 0.2747 (0.2747) loss_classifier: 0.0861 (0.0861) loss_box_reg: 0.1769 (0.1769) loss_objectness: 0.0057 (0.0057) loss_rpn_box_reg: 0.0060 (0.0060) time: 1.7233 data: 0.7040 max mem: 4355 Epoch: [9] [ 10/104] eta: 0:01:37 lr: 0.000005 loss: 0.1894 (0.1870) loss_classifier: 0.0490 (0.0523) loss_box_reg: 0.1344 (0.1286) loss_objectness: 0.0014 (0.0021) loss_rpn_box_reg: 0.0044 (0.0040) time: 1.0415 data: 0.0802 max mem: 4355 Epoch: [9] [ 20/104] eta: 0:01:25 lr: 0.000005 loss: 0.1927 (0.2020) loss_classifier: 0.0524 (0.0572) loss_box_reg: 0.1344 (0.1372) loss_objectness: 0.0014 (0.0024) loss_rpn_box_reg: 0.0044 (0.0053) time: 0.9822 data: 0.0212 max mem: 4355 Epoch: [9] [ 30/104] eta: 0:01:14 lr: 0.000005 loss: 0.2098 (0.2164) loss_classifier: 0.0611 (0.0615) loss_box_reg: 0.1394 (0.1464) loss_objectness: 0.0016 (0.0024) loss_rpn_box_reg: 0.0066 (0.0061) time: 0.9863 data: 0.0233 max mem: 4355 Epoch: [9] [ 40/104] eta: 0:01:03 lr: 0.000005 loss: 0.1981 (0.2144) loss_classifier: 0.0548 (0.0604) loss_box_reg: 0.1372 (0.1452) loss_objectness: 0.0016 (0.0024) loss_rpn_box_reg: 0.0044 (0.0064) time: 0.9718 data: 0.0216 max mem: 4355 Epoch: [9] [ 50/104] eta: 0:00:53 lr: 0.000005 loss: 0.1892 (0.2187) loss_classifier: 0.0589 (0.0623) loss_box_reg: 0.1184 (0.1477) loss_objectness: 0.0016 (0.0023) loss_rpn_box_reg: 0.0045 (0.0064) time: 0.9592 data: 0.0218 max mem: 4355 Epoch: [9] [ 60/104] eta: 0:00:43 lr: 0.000005 loss: 0.2039 (0.2182) loss_classifier: 0.0611 (0.0622) loss_box_reg: 0.1382 (0.1475) loss_objectness: 0.0013 (0.0022) loss_rpn_box_reg: 0.0050 (0.0064) time: 0.9484 data: 0.0203 max mem: 4355 Epoch: [9] [ 70/104] eta: 0:00:33 lr: 0.000005 loss: 0.1871 (0.2166) loss_classifier: 0.0535 (0.0612) loss_box_reg: 0.1327 (0.1469) loss_objectness: 0.0010 (0.0021) loss_rpn_box_reg: 0.0047 (0.0064) time: 0.9433 data: 0.0204 max mem: 4355 Epoch: [9] [ 80/104] eta: 0:00:23 lr: 0.000005 loss: 0.1871 (0.2146) loss_classifier: 0.0535 (0.0604) loss_box_reg: 0.1312 (0.1457) loss_objectness: 0.0012 (0.0021) loss_rpn_box_reg: 0.0055 (0.0064) time: 0.9457 data: 0.0215 max mem: 4355 Epoch: [9] [ 90/104] eta: 0:00:13 lr: 0.000005 loss: 0.2013 (0.2137) loss_classifier: 0.0550 (0.0605) loss_box_reg: 0.1380 (0.1448) loss_objectness: 0.0015 (0.0020) loss_rpn_box_reg: 0.0062 (0.0064) time: 0.9451 data: 0.0196 max mem: 4355 Epoch: [9] [100/104] eta: 0:00:03 lr: 0.000005 loss: 0.2118 (0.2138) loss_classifier: 0.0558 (0.0606) loss_box_reg: 0.1422 (0.1448) loss_objectness: 0.0015 (0.0020) loss_rpn_box_reg: 0.0062 (0.0064) time: 0.9498 data: 0.0186 max mem: 4355 Epoch: [9] [103/104] eta: 0:00:00 lr: 0.000005 loss: 0.1894 (0.2135) loss_classifier: 0.0547 (0.0606) loss_box_reg: 0.1295 (0.1444) loss_objectness: 0.0014 (0.0020) loss_rpn_box_reg: 0.0052 (0.0064) time: 0.9504 data: 0.0183 max mem: 4355 Epoch: [9] Total time: 0:01:40 (0.9676 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:27 model_time: 0.4471 (0.4471) evaluator_time: 0.0167 (0.0167) time: 1.0498 data: 0.5740 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4034 (0.4060) evaluator_time: 0.0125 (0.0186) time: 0.4467 data: 0.0193 max mem: 4355 Test: Total time: 0:00:12 (0.4761 s / it) Averaged stats: model_time: 0.4034 (0.4060) evaluator_time: 0.0125 (0.0186) Accumulating evaluation results... DONE (t=0.07s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.891 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.599 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.483 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.576 Epoch: [10] [ 0/104] eta: 0:03:00 lr: 0.000005 loss: 0.2670 (0.2670) loss_classifier: 0.0906 (0.0906) loss_box_reg: 0.1686 (0.1686) loss_objectness: 0.0016 (0.0016) loss_rpn_box_reg: 0.0063 (0.0063) time: 1.7310 data: 0.7506 max mem: 4355 Epoch: [10] [ 10/104] eta: 0:01:37 lr: 0.000005 loss: 0.2108 (0.2081) loss_classifier: 0.0610 (0.0627) loss_box_reg: 0.1423 (0.1377) loss_objectness: 0.0014 (0.0018) loss_rpn_box_reg: 0.0062 (0.0059) time: 1.0400 data: 0.0838 max mem: 4355 Epoch: [10] [ 20/104] eta: 0:01:25 lr: 0.000005 loss: 0.2108 (0.2098) loss_classifier: 0.0595 (0.0606) loss_box_reg: 0.1423 (0.1416) loss_objectness: 0.0012 (0.0016) loss_rpn_box_reg: 0.0058 (0.0061) time: 0.9781 data: 0.0190 max mem: 4355 Epoch: [10] [ 30/104] eta: 0:01:14 lr: 0.000005 loss: 0.2136 (0.2086) loss_classifier: 0.0644 (0.0600) loss_box_reg: 0.1432 (0.1405) loss_objectness: 0.0014 (0.0018) loss_rpn_box_reg: 0.0056 (0.0063) time: 0.9801 data: 0.0208 max mem: 4355 Epoch: [10] [ 40/104] eta: 0:01:03 lr: 0.000005 loss: 0.1992 (0.2072) loss_classifier: 0.0568 (0.0598) loss_box_reg: 0.1350 (0.1397) loss_objectness: 0.0013 (0.0017) loss_rpn_box_reg: 0.0039 (0.0059) time: 0.9665 data: 0.0199 max mem: 4355 Epoch: [10] [ 50/104] eta: 0:00:53 lr: 0.000005 loss: 0.2019 (0.2115) loss_classifier: 0.0568 (0.0614) loss_box_reg: 0.1417 (0.1420) loss_objectness: 0.0010 (0.0017) loss_rpn_box_reg: 0.0048 (0.0063) time: 0.9574 data: 0.0201 max mem: 4355 Epoch: [10] [ 60/104] eta: 0:00:43 lr: 0.000005 loss: 0.2197 (0.2124) loss_classifier: 0.0627 (0.0620) loss_box_reg: 0.1424 (0.1422) loss_objectness: 0.0014 (0.0018) loss_rpn_box_reg: 0.0053 (0.0064) time: 0.9508 data: 0.0204 max mem: 4355 Epoch: [10] [ 70/104] eta: 0:00:33 lr: 0.000005 loss: 0.2106 (0.2114) loss_classifier: 0.0603 (0.0614) loss_box_reg: 0.1370 (0.1418) loss_objectness: 0.0018 (0.0019) loss_rpn_box_reg: 0.0050 (0.0063) time: 0.9439 data: 0.0194 max mem: 4355 Epoch: [10] [ 80/104] eta: 0:00:23 lr: 0.000005 loss: 0.1949 (0.2139) loss_classifier: 0.0611 (0.0620) loss_box_reg: 0.1414 (0.1436) loss_objectness: 0.0018 (0.0019) loss_rpn_box_reg: 0.0056 (0.0063) time: 0.9463 data: 0.0203 max mem: 4355 Epoch: [10] [ 90/104] eta: 0:00:13 lr: 0.000005 loss: 0.2320 (0.2142) loss_classifier: 0.0630 (0.0619) loss_box_reg: 0.1552 (0.1440) loss_objectness: 0.0010 (0.0019) loss_rpn_box_reg: 0.0054 (0.0065) time: 0.9535 data: 0.0220 max mem: 4355 Epoch: [10] [100/104] eta: 0:00:03 lr: 0.000005 loss: 0.2157 (0.2144) loss_classifier: 0.0597 (0.0614) loss_box_reg: 0.1539 (0.1447) loss_objectness: 0.0017 (0.0019) loss_rpn_box_reg: 0.0049 (0.0064) time: 0.9513 data: 0.0200 max mem: 4355 Epoch: [10] [103/104] eta: 0:00:00 lr: 0.000005 loss: 0.1912 (0.2138) loss_classifier: 0.0562 (0.0614) loss_box_reg: 0.1265 (0.1441) loss_objectness: 0.0012 (0.0019) loss_rpn_box_reg: 0.0047 (0.0064) time: 0.9544 data: 0.0200 max mem: 4355 Epoch: [10] Total time: 0:01:40 (0.9676 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:51 model_time: 0.5838 (0.5838) evaluator_time: 0.0634 (0.0634) time: 1.9818 data: 1.3005 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4025 (0.4101) evaluator_time: 0.0123 (0.0182) time: 0.4417 data: 0.0180 max mem: 4355 Test: Total time: 0:00:13 (0.5079 s / it) Averaged stats: model_time: 0.4025 (0.4101) evaluator_time: 0.0123 (0.0182) Accumulating evaluation results... DONE (t=0.14s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.537 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.891 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.599 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.483 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.576 Epoch: [11] [ 0/104] eta: 0:03:53 lr: 0.000005 loss: 0.1864 (0.1864) loss_classifier: 0.0484 (0.0484) loss_box_reg: 0.1316 (0.1316) loss_objectness: 0.0012 (0.0012) loss_rpn_box_reg: 0.0052 (0.0052) time: 2.2469 data: 1.0963 max mem: 4355 Epoch: [11] [ 10/104] eta: 0:01:41 lr: 0.000005 loss: 0.1747 (0.1639) loss_classifier: 0.0476 (0.0478) loss_box_reg: 0.1178 (0.1094) loss_objectness: 0.0012 (0.0023) loss_rpn_box_reg: 0.0047 (0.0044) time: 1.0772 data: 0.1148 max mem: 4355 Epoch: [11] [ 20/104] eta: 0:01:26 lr: 0.000005 loss: 0.1747 (0.1792) loss_classifier: 0.0476 (0.0501) loss_box_reg: 0.1197 (0.1222) loss_objectness: 0.0013 (0.0022) loss_rpn_box_reg: 0.0045 (0.0047) time: 0.9732 data: 0.0179 max mem: 4355 Epoch: [11] [ 30/104] eta: 0:01:15 lr: 0.000005 loss: 0.1969 (0.1938) loss_classifier: 0.0587 (0.0544) loss_box_reg: 0.1443 (0.1322) loss_objectness: 0.0014 (0.0021) loss_rpn_box_reg: 0.0052 (0.0051) time: 0.9813 data: 0.0203 max mem: 4355 Epoch: [11] [ 40/104] eta: 0:01:04 lr: 0.000005 loss: 0.2063 (0.1954) loss_classifier: 0.0594 (0.0556) loss_box_reg: 0.1454 (0.1324) loss_objectness: 0.0016 (0.0022) loss_rpn_box_reg: 0.0053 (0.0052) time: 0.9670 data: 0.0202 max mem: 4355 Epoch: [11] [ 50/104] eta: 0:00:53 lr: 0.000005 loss: 0.2383 (0.2033) loss_classifier: 0.0673 (0.0578) loss_box_reg: 0.1593 (0.1375) loss_objectness: 0.0021 (0.0023) loss_rpn_box_reg: 0.0058 (0.0057) time: 0.9729 data: 0.0242 max mem: 4355 Epoch: [11] [ 60/104] eta: 0:00:43 lr: 0.000005 loss: 0.2416 (0.2062) loss_classifier: 0.0650 (0.0582) loss_box_reg: 0.1623 (0.1399) loss_objectness: 0.0024 (0.0023) loss_rpn_box_reg: 0.0072 (0.0058) time: 0.9699 data: 0.0266 max mem: 4355 Epoch: [11] [ 70/104] eta: 0:00:33 lr: 0.000005 loss: 0.2074 (0.2100) loss_classifier: 0.0581 (0.0596) loss_box_reg: 0.1457 (0.1421) loss_objectness: 0.0015 (0.0022) loss_rpn_box_reg: 0.0064 (0.0061) time: 0.9442 data: 0.0215 max mem: 4355 Epoch: [11] [ 80/104] eta: 0:00:23 lr: 0.000005 loss: 0.2106 (0.2102) loss_classifier: 0.0583 (0.0594) loss_box_reg: 0.1457 (0.1422) loss_objectness: 0.0015 (0.0022) loss_rpn_box_reg: 0.0058 (0.0063) time: 0.9448 data: 0.0211 max mem: 4355 Epoch: [11] [ 90/104] eta: 0:00:13 lr: 0.000005 loss: 0.2203 (0.2131) loss_classifier: 0.0692 (0.0608) loss_box_reg: 0.1496 (0.1436) loss_objectness: 0.0014 (0.0023) loss_rpn_box_reg: 0.0054 (0.0064) time: 0.9538 data: 0.0220 max mem: 4355 Epoch: [11] [100/104] eta: 0:00:03 lr: 0.000005 loss: 0.2337 (0.2140) loss_classifier: 0.0699 (0.0611) loss_box_reg: 0.1496 (0.1442) loss_objectness: 0.0014 (0.0022) loss_rpn_box_reg: 0.0060 (0.0064) time: 0.9520 data: 0.0196 max mem: 4355 Epoch: [11] [103/104] eta: 0:00:00 lr: 0.000005 loss: 0.2203 (0.2137) loss_classifier: 0.0644 (0.0612) loss_box_reg: 0.1460 (0.1439) loss_objectness: 0.0014 (0.0022) loss_rpn_box_reg: 0.0056 (0.0064) time: 0.9556 data: 0.0197 max mem: 4355 Epoch: [11] Total time: 0:01:41 (0.9749 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:34 model_time: 0.4899 (0.4899) evaluator_time: 0.0167 (0.0167) time: 1.3324 data: 0.8204 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4024 (0.4067) evaluator_time: 0.0100 (0.0185) time: 0.4487 data: 0.0199 max mem: 4355 Test: Total time: 0:00:12 (0.4844 s / it) Averaged stats: model_time: 0.4024 (0.4067) evaluator_time: 0.0100 (0.0185) Accumulating evaluation results... DONE (t=0.14s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.891 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.599 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577 Epoch: [12] [ 0/104] eta: 0:03:13 lr: 0.000001 loss: 0.3097 (0.3097) loss_classifier: 0.0874 (0.0874) loss_box_reg: 0.2138 (0.2138) loss_objectness: 0.0014 (0.0014) loss_rpn_box_reg: 0.0070 (0.0070) time: 1.8569 data: 0.8821 max mem: 4355 Epoch: [12] [ 10/104] eta: 0:01:38 lr: 0.000001 loss: 0.2367 (0.2286) loss_classifier: 0.0637 (0.0666) loss_box_reg: 0.1536 (0.1514) loss_objectness: 0.0015 (0.0026) loss_rpn_box_reg: 0.0074 (0.0080) time: 1.0436 data: 0.0948 max mem: 4355 Epoch: [12] [ 20/104] eta: 0:01:25 lr: 0.000001 loss: 0.2173 (0.2200) loss_classifier: 0.0605 (0.0612) loss_box_reg: 0.1486 (0.1487) loss_objectness: 0.0012 (0.0021) loss_rpn_box_reg: 0.0070 (0.0080) time: 0.9735 data: 0.0185 max mem: 4355 Epoch: [12] [ 30/104] eta: 0:01:14 lr: 0.000001 loss: 0.2139 (0.2248) loss_classifier: 0.0637 (0.0638) loss_box_reg: 0.1435 (0.1506) loss_objectness: 0.0015 (0.0025) loss_rpn_box_reg: 0.0047 (0.0078) time: 0.9810 data: 0.0220 max mem: 4355 Epoch: [12] [ 40/104] eta: 0:01:03 lr: 0.000001 loss: 0.2034 (0.2156) loss_classifier: 0.0597 (0.0609) loss_box_reg: 0.1373 (0.1452) loss_objectness: 0.0020 (0.0023) loss_rpn_box_reg: 0.0046 (0.0072) time: 0.9726 data: 0.0230 max mem: 4355 Epoch: [12] [ 50/104] eta: 0:00:53 lr: 0.000001 loss: 0.1979 (0.2145) loss_classifier: 0.0558 (0.0613) loss_box_reg: 0.1276 (0.1441) loss_objectness: 0.0014 (0.0023) loss_rpn_box_reg: 0.0047 (0.0069) time: 0.9568 data: 0.0208 max mem: 4355 Epoch: [12] [ 60/104] eta: 0:00:43 lr: 0.000001 loss: 0.2116 (0.2162) loss_classifier: 0.0632 (0.0620) loss_box_reg: 0.1338 (0.1453) loss_objectness: 0.0019 (0.0023) loss_rpn_box_reg: 0.0047 (0.0067) time: 0.9486 data: 0.0195 max mem: 4355 Epoch: [12] [ 70/104] eta: 0:00:33 lr: 0.000001 loss: 0.2140 (0.2137) loss_classifier: 0.0632 (0.0611) loss_box_reg: 0.1433 (0.1439) loss_objectness: 0.0019 (0.0022) loss_rpn_box_reg: 0.0047 (0.0065) time: 0.9485 data: 0.0200 max mem: 4355 Epoch: [12] [ 80/104] eta: 0:00:23 lr: 0.000001 loss: 0.2154 (0.2122) loss_classifier: 0.0562 (0.0606) loss_box_reg: 0.1433 (0.1430) loss_objectness: 0.0020 (0.0022) loss_rpn_box_reg: 0.0053 (0.0064) time: 0.9514 data: 0.0216 max mem: 4355 Epoch: [12] [ 90/104] eta: 0:00:13 lr: 0.000001 loss: 0.1897 (0.2136) loss_classifier: 0.0550 (0.0612) loss_box_reg: 0.1214 (0.1437) loss_objectness: 0.0020 (0.0022) loss_rpn_box_reg: 0.0053 (0.0065) time: 0.9539 data: 0.0213 max mem: 4355 Epoch: [12] [100/104] eta: 0:00:03 lr: 0.000001 loss: 0.1897 (0.2126) loss_classifier: 0.0550 (0.0609) loss_box_reg: 0.1214 (0.1431) loss_objectness: 0.0023 (0.0022) loss_rpn_box_reg: 0.0047 (0.0064) time: 0.9551 data: 0.0203 max mem: 4355 Epoch: [12] [103/104] eta: 0:00:00 lr: 0.000001 loss: 0.2109 (0.2136) loss_classifier: 0.0550 (0.0610) loss_box_reg: 0.1340 (0.1440) loss_objectness: 0.0023 (0.0022) loss_rpn_box_reg: 0.0045 (0.0064) time: 0.9519 data: 0.0196 max mem: 4355 Epoch: [12] Total time: 0:01:40 (0.9695 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:29 model_time: 0.4803 (0.4803) evaluator_time: 0.0165 (0.0165) time: 1.1303 data: 0.6133 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4036 (0.4066) evaluator_time: 0.0138 (0.0333) time: 0.4699 data: 0.0200 max mem: 4355 Test: Total time: 0:00:12 (0.4934 s / it) Averaged stats: model_time: 0.4036 (0.4066) evaluator_time: 0.0138 (0.0333) Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.891 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.599 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577 Epoch: [13] [ 0/104] eta: 0:02:59 lr: 0.000001 loss: 0.3098 (0.3098) loss_classifier: 0.0753 (0.0753) loss_box_reg: 0.2196 (0.2196) loss_objectness: 0.0041 (0.0041) loss_rpn_box_reg: 0.0108 (0.0108) time: 1.7234 data: 0.7536 max mem: 4355 Epoch: [13] [ 10/104] eta: 0:01:37 lr: 0.000001 loss: 0.2109 (0.2131) loss_classifier: 0.0570 (0.0617) loss_box_reg: 0.1349 (0.1434) loss_objectness: 0.0015 (0.0020) loss_rpn_box_reg: 0.0058 (0.0061) time: 1.0362 data: 0.0849 max mem: 4355 Epoch: [13] [ 20/104] eta: 0:01:24 lr: 0.000001 loss: 0.1885 (0.2113) loss_classifier: 0.0562 (0.0616) loss_box_reg: 0.1265 (0.1410) loss_objectness: 0.0013 (0.0021) loss_rpn_box_reg: 0.0049 (0.0066) time: 0.9726 data: 0.0186 max mem: 4355 Epoch: [13] [ 30/104] eta: 0:01:13 lr: 0.000001 loss: 0.1926 (0.2183) loss_classifier: 0.0510 (0.0636) loss_box_reg: 0.1303 (0.1458) loss_objectness: 0.0018 (0.0021) loss_rpn_box_reg: 0.0049 (0.0068) time: 0.9742 data: 0.0205 max mem: 4355 Epoch: [13] [ 40/104] eta: 0:01:03 lr: 0.000001 loss: 0.2001 (0.2155) loss_classifier: 0.0581 (0.0621) loss_box_reg: 0.1363 (0.1443) loss_objectness: 0.0018 (0.0022) loss_rpn_box_reg: 0.0054 (0.0069) time: 0.9651 data: 0.0220 max mem: 4355 Epoch: [13] [ 50/104] eta: 0:00:52 lr: 0.000001 loss: 0.1935 (0.2104) loss_classifier: 0.0557 (0.0600) loss_box_reg: 0.1278 (0.1416) loss_objectness: 0.0015 (0.0020) loss_rpn_box_reg: 0.0054 (0.0068) time: 0.9567 data: 0.0220 max mem: 4355 Epoch: [13] [ 60/104] eta: 0:00:42 lr: 0.000001 loss: 0.1718 (0.2059) loss_classifier: 0.0490 (0.0592) loss_box_reg: 0.1156 (0.1384) loss_objectness: 0.0008 (0.0018) loss_rpn_box_reg: 0.0043 (0.0065) time: 0.9447 data: 0.0201 max mem: 4355 Epoch: [13] [ 70/104] eta: 0:00:32 lr: 0.000001 loss: 0.1823 (0.2090) loss_classifier: 0.0546 (0.0602) loss_box_reg: 0.1268 (0.1405) loss_objectness: 0.0011 (0.0018) loss_rpn_box_reg: 0.0048 (0.0064) time: 0.9419 data: 0.0202 max mem: 4355 Epoch: [13] [ 80/104] eta: 0:00:23 lr: 0.000001 loss: 0.2312 (0.2116) loss_classifier: 0.0644 (0.0608) loss_box_reg: 0.1585 (0.1424) loss_objectness: 0.0016 (0.0018) loss_rpn_box_reg: 0.0061 (0.0067) time: 0.9495 data: 0.0210 max mem: 4355 Epoch: [13] [ 90/104] eta: 0:00:13 lr: 0.000001 loss: 0.2416 (0.2127) loss_classifier: 0.0693 (0.0615) loss_box_reg: 0.1554 (0.1429) loss_objectness: 0.0014 (0.0018) loss_rpn_box_reg: 0.0044 (0.0065) time: 0.9490 data: 0.0198 max mem: 4355 Epoch: [13] [100/104] eta: 0:00:03 lr: 0.000001 loss: 0.2387 (0.2133) loss_classifier: 0.0700 (0.0615) loss_box_reg: 0.1548 (0.1435) loss_objectness: 0.0011 (0.0018) loss_rpn_box_reg: 0.0044 (0.0065) time: 0.9502 data: 0.0191 max mem: 4355 Epoch: [13] [103/104] eta: 0:00:00 lr: 0.000001 loss: 0.2383 (0.2135) loss_classifier: 0.0692 (0.0615) loss_box_reg: 0.1554 (0.1438) loss_objectness: 0.0011 (0.0018) loss_rpn_box_reg: 0.0058 (0.0064) time: 0.9506 data: 0.0187 max mem: 4355 Epoch: [13] Total time: 0:01:40 (0.9644 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:31 model_time: 0.4894 (0.4894) evaluator_time: 0.0180 (0.0180) time: 1.1968 data: 0.6822 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4034 (0.4081) evaluator_time: 0.0100 (0.0187) time: 0.4459 data: 0.0193 max mem: 4355 Test: Total time: 0:00:12 (0.4806 s / it) Averaged stats: model_time: 0.4034 (0.4081) evaluator_time: 0.0100 (0.0187) Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.891 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.599 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577 Epoch: [14] [ 0/104] eta: 0:02:54 lr: 0.000001 loss: 0.2427 (0.2427) loss_classifier: 0.0751 (0.0751) loss_box_reg: 0.1627 (0.1627) loss_objectness: 0.0006 (0.0006) loss_rpn_box_reg: 0.0043 (0.0043) time: 1.6752 data: 0.6798 max mem: 4355 Epoch: [14] [ 10/104] eta: 0:01:36 lr: 0.000001 loss: 0.2261 (0.2072) loss_classifier: 0.0647 (0.0572) loss_box_reg: 0.1519 (0.1405) loss_objectness: 0.0008 (0.0024) loss_rpn_box_reg: 0.0048 (0.0071) time: 1.0307 data: 0.0786 max mem: 4355 Epoch: [14] [ 20/104] eta: 0:01:24 lr: 0.000001 loss: 0.2235 (0.2199) loss_classifier: 0.0629 (0.0620) loss_box_reg: 0.1528 (0.1485) loss_objectness: 0.0008 (0.0022) loss_rpn_box_reg: 0.0051 (0.0071) time: 0.9756 data: 0.0211 max mem: 4355 Epoch: [14] [ 30/104] eta: 0:01:13 lr: 0.000001 loss: 0.2235 (0.2202) loss_classifier: 0.0585 (0.0622) loss_box_reg: 0.1496 (0.1486) loss_objectness: 0.0018 (0.0025) loss_rpn_box_reg: 0.0043 (0.0069) time: 0.9759 data: 0.0210 max mem: 4355 Epoch: [14] [ 40/104] eta: 0:01:03 lr: 0.000001 loss: 0.1953 (0.2185) loss_classifier: 0.0582 (0.0618) loss_box_reg: 0.1366 (0.1468) loss_objectness: 0.0026 (0.0028) loss_rpn_box_reg: 0.0045 (0.0071) time: 0.9610 data: 0.0186 max mem: 4355 Epoch: [14] [ 50/104] eta: 0:00:52 lr: 0.000001 loss: 0.1914 (0.2175) loss_classifier: 0.0642 (0.0619) loss_box_reg: 0.1367 (0.1458) loss_objectness: 0.0026 (0.0028) loss_rpn_box_reg: 0.0050 (0.0070) time: 0.9588 data: 0.0213 max mem: 4355 Epoch: [14] [ 60/104] eta: 0:00:42 lr: 0.000001 loss: 0.1854 (0.2098) loss_classifier: 0.0529 (0.0600) loss_box_reg: 0.1270 (0.1409) loss_objectness: 0.0008 (0.0025) loss_rpn_box_reg: 0.0043 (0.0065) time: 0.9557 data: 0.0232 max mem: 4355 Epoch: [14] [ 70/104] eta: 0:00:32 lr: 0.000001 loss: 0.1882 (0.2125) loss_classifier: 0.0543 (0.0605) loss_box_reg: 0.1279 (0.1429) loss_objectness: 0.0011 (0.0025) loss_rpn_box_reg: 0.0038 (0.0066) time: 0.9438 data: 0.0207 max mem: 4355 Epoch: [14] [ 80/104] eta: 0:00:23 lr: 0.000001 loss: 0.2475 (0.2164) loss_classifier: 0.0678 (0.0615) loss_box_reg: 0.1601 (0.1456) loss_objectness: 0.0017 (0.0025) loss_rpn_box_reg: 0.0057 (0.0067) time: 0.9433 data: 0.0192 max mem: 4355 Epoch: [14] [ 90/104] eta: 0:00:13 lr: 0.000001 loss: 0.2331 (0.2157) loss_classifier: 0.0657 (0.0614) loss_box_reg: 0.1549 (0.1452) loss_objectness: 0.0014 (0.0025) loss_rpn_box_reg: 0.0056 (0.0066) time: 0.9524 data: 0.0222 max mem: 4355 Epoch: [14] [100/104] eta: 0:00:03 lr: 0.000001 loss: 0.1883 (0.2135) loss_classifier: 0.0579 (0.0609) loss_box_reg: 0.1299 (0.1437) loss_objectness: 0.0015 (0.0024) loss_rpn_box_reg: 0.0050 (0.0064) time: 0.9505 data: 0.0214 max mem: 4355 Epoch: [14] [103/104] eta: 0:00:00 lr: 0.000001 loss: 0.1883 (0.2133) loss_classifier: 0.0579 (0.0606) loss_box_reg: 0.1299 (0.1439) loss_objectness: 0.0012 (0.0024) loss_rpn_box_reg: 0.0050 (0.0064) time: 0.9526 data: 0.0214 max mem: 4355 Epoch: [14] Total time: 0:01:40 (0.9658 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:40 model_time: 0.6157 (0.6157) evaluator_time: 0.0845 (0.0845) time: 1.5539 data: 0.8393 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4014 (0.4104) evaluator_time: 0.0100 (0.0185) time: 0.4420 data: 0.0185 max mem: 4355 Test: Total time: 0:00:12 (0.4922 s / it) Averaged stats: model_time: 0.4014 (0.4104) evaluator_time: 0.0100 (0.0185) Accumulating evaluation results... DONE (t=0.12s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.891 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.599 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577 Epoch: [15] [ 0/104] eta: 0:03:45 lr: 0.000000 loss: 0.3076 (0.3076) loss_classifier: 0.0903 (0.0903) loss_box_reg: 0.2101 (0.2101) loss_objectness: 0.0010 (0.0010) loss_rpn_box_reg: 0.0063 (0.0063) time: 2.1664 data: 1.0755 max mem: 4355 Epoch: [15] [ 10/104] eta: 0:01:40 lr: 0.000000 loss: 0.2037 (0.2317) loss_classifier: 0.0603 (0.0686) loss_box_reg: 0.1365 (0.1550) loss_objectness: 0.0018 (0.0020) loss_rpn_box_reg: 0.0063 (0.0060) time: 1.0684 data: 0.1131 max mem: 4355 Epoch: [15] [ 20/104] eta: 0:01:26 lr: 0.000000 loss: 0.2037 (0.2205) loss_classifier: 0.0603 (0.0652) loss_box_reg: 0.1371 (0.1478) loss_objectness: 0.0017 (0.0019) loss_rpn_box_reg: 0.0051 (0.0056) time: 0.9718 data: 0.0202 max mem: 4355 Epoch: [15] [ 30/104] eta: 0:01:15 lr: 0.000000 loss: 0.2189 (0.2210) loss_classifier: 0.0627 (0.0646) loss_box_reg: 0.1433 (0.1484) loss_objectness: 0.0015 (0.0021) loss_rpn_box_reg: 0.0051 (0.0058) time: 0.9902 data: 0.0238 max mem: 4355 Epoch: [15] [ 40/104] eta: 0:01:04 lr: 0.000000 loss: 0.2099 (0.2180) loss_classifier: 0.0578 (0.0639) loss_box_reg: 0.1419 (0.1461) loss_objectness: 0.0022 (0.0022) loss_rpn_box_reg: 0.0054 (0.0058) time: 0.9815 data: 0.0229 max mem: 4355 Epoch: [15] [ 50/104] eta: 0:00:53 lr: 0.000000 loss: 0.2060 (0.2146) loss_classifier: 0.0539 (0.0623) loss_box_reg: 0.1430 (0.1444) loss_objectness: 0.0013 (0.0021) loss_rpn_box_reg: 0.0045 (0.0059) time: 0.9626 data: 0.0215 max mem: 4355 Epoch: [15] [ 60/104] eta: 0:00:43 lr: 0.000000 loss: 0.2060 (0.2138) loss_classifier: 0.0557 (0.0615) loss_box_reg: 0.1471 (0.1443) loss_objectness: 0.0013 (0.0021) loss_rpn_box_reg: 0.0052 (0.0060) time: 0.9545 data: 0.0226 max mem: 4355 Epoch: [15] [ 70/104] eta: 0:00:33 lr: 0.000000 loss: 0.2034 (0.2134) loss_classifier: 0.0616 (0.0615) loss_box_reg: 0.1341 (0.1440) loss_objectness: 0.0016 (0.0020) loss_rpn_box_reg: 0.0052 (0.0059) time: 0.9466 data: 0.0219 max mem: 4355 Epoch: [15] [ 80/104] eta: 0:00:23 lr: 0.000000 loss: 0.2017 (0.2127) loss_classifier: 0.0611 (0.0612) loss_box_reg: 0.1341 (0.1435) loss_objectness: 0.0018 (0.0021) loss_rpn_box_reg: 0.0052 (0.0060) time: 0.9463 data: 0.0213 max mem: 4355 Epoch: [15] [ 90/104] eta: 0:00:13 lr: 0.000000 loss: 0.2017 (0.2118) loss_classifier: 0.0522 (0.0607) loss_box_reg: 0.1420 (0.1430) loss_objectness: 0.0021 (0.0021) loss_rpn_box_reg: 0.0056 (0.0060) time: 0.9546 data: 0.0213 max mem: 4355 Epoch: [15] [100/104] eta: 0:00:03 lr: 0.000000 loss: 0.2206 (0.2151) loss_classifier: 0.0644 (0.0614) loss_box_reg: 0.1520 (0.1450) loss_objectness: 0.0020 (0.0021) loss_rpn_box_reg: 0.0056 (0.0065) time: 0.9571 data: 0.0198 max mem: 4355 Epoch: [15] [103/104] eta: 0:00:00 lr: 0.000000 loss: 0.2225 (0.2134) loss_classifier: 0.0644 (0.0609) loss_box_reg: 0.1626 (0.1440) loss_objectness: 0.0018 (0.0021) loss_rpn_box_reg: 0.0049 (0.0064) time: 0.9587 data: 0.0196 max mem: 4355 Epoch: [15] Total time: 0:01:41 (0.9747 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:31 model_time: 0.4641 (0.4641) evaluator_time: 0.0199 (0.0199) time: 1.2298 data: 0.7273 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4043 (0.4066) evaluator_time: 0.0133 (0.0185) time: 0.4477 data: 0.0186 max mem: 4355 Test: Total time: 0:00:12 (0.4800 s / it) Averaged stats: model_time: 0.4043 (0.4066) evaluator_time: 0.0133 (0.0185) Accumulating evaluation results... DONE (t=0.07s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.891 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.599 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577 Epoch: [16] [ 0/104] eta: 0:02:51 lr: 0.000000 loss: 0.0866 (0.0866) loss_classifier: 0.0232 (0.0232) loss_box_reg: 0.0614 (0.0614) loss_objectness: 0.0005 (0.0005) loss_rpn_box_reg: 0.0015 (0.0015) time: 1.6520 data: 0.6281 max mem: 4355 Epoch: [16] [ 10/104] eta: 0:01:37 lr: 0.000000 loss: 0.1753 (0.1950) loss_classifier: 0.0504 (0.0575) loss_box_reg: 0.1141 (0.1307) loss_objectness: 0.0006 (0.0015) loss_rpn_box_reg: 0.0042 (0.0054) time: 1.0382 data: 0.0734 max mem: 4355 Epoch: [16] [ 20/104] eta: 0:01:24 lr: 0.000000 loss: 0.1802 (0.2053) loss_classifier: 0.0531 (0.0599) loss_box_reg: 0.1238 (0.1377) loss_objectness: 0.0008 (0.0019) loss_rpn_box_reg: 0.0042 (0.0057) time: 0.9785 data: 0.0188 max mem: 4355 Epoch: [16] [ 30/104] eta: 0:01:13 lr: 0.000000 loss: 0.1734 (0.1981) loss_classifier: 0.0502 (0.0569) loss_box_reg: 0.1219 (0.1338) loss_objectness: 0.0013 (0.0017) loss_rpn_box_reg: 0.0040 (0.0057) time: 0.9776 data: 0.0202 max mem: 4355 Epoch: [16] [ 40/104] eta: 0:01:03 lr: 0.000000 loss: 0.1840 (0.2058) loss_classifier: 0.0507 (0.0588) loss_box_reg: 0.1243 (0.1390) loss_objectness: 0.0009 (0.0017) loss_rpn_box_reg: 0.0039 (0.0064) time: 0.9678 data: 0.0204 max mem: 4355 Epoch: [16] [ 50/104] eta: 0:00:52 lr: 0.000000 loss: 0.1912 (0.2074) loss_classifier: 0.0547 (0.0595) loss_box_reg: 0.1329 (0.1397) loss_objectness: 0.0011 (0.0017) loss_rpn_box_reg: 0.0049 (0.0065) time: 0.9503 data: 0.0191 max mem: 4355 Epoch: [16] [ 60/104] eta: 0:00:42 lr: 0.000000 loss: 0.1912 (0.2080) loss_classifier: 0.0547 (0.0593) loss_box_reg: 0.1346 (0.1405) loss_objectness: 0.0015 (0.0019) loss_rpn_box_reg: 0.0060 (0.0064) time: 0.9435 data: 0.0195 max mem: 4355 Epoch: [16] [ 70/104] eta: 0:00:32 lr: 0.000000 loss: 0.1956 (0.2082) loss_classifier: 0.0541 (0.0594) loss_box_reg: 0.1346 (0.1406) loss_objectness: 0.0015 (0.0019) loss_rpn_box_reg: 0.0047 (0.0064) time: 0.9442 data: 0.0200 max mem: 4355 Epoch: [16] [ 80/104] eta: 0:00:23 lr: 0.000000 loss: 0.2074 (0.2137) loss_classifier: 0.0587 (0.0613) loss_box_reg: 0.1411 (0.1437) loss_objectness: 0.0020 (0.0021) loss_rpn_box_reg: 0.0062 (0.0066) time: 0.9428 data: 0.0193 max mem: 4355 Epoch: [16] [ 90/104] eta: 0:00:13 lr: 0.000000 loss: 0.2074 (0.2114) loss_classifier: 0.0661 (0.0608) loss_box_reg: 0.1402 (0.1422) loss_objectness: 0.0014 (0.0020) loss_rpn_box_reg: 0.0049 (0.0063) time: 0.9482 data: 0.0199 max mem: 4355 Epoch: [16] [100/104] eta: 0:00:03 lr: 0.000000 loss: 0.1901 (0.2125) loss_classifier: 0.0544 (0.0607) loss_box_reg: 0.1276 (0.1434) loss_objectness: 0.0012 (0.0019) loss_rpn_box_reg: 0.0048 (0.0064) time: 0.9532 data: 0.0198 max mem: 4355 Epoch: [16] [103/104] eta: 0:00:00 lr: 0.000000 loss: 0.1901 (0.2129) loss_classifier: 0.0544 (0.0608) loss_box_reg: 0.1276 (0.1438) loss_objectness: 0.0013 (0.0020) loss_rpn_box_reg: 0.0052 (0.0064) time: 0.9481 data: 0.0183 max mem: 4355 Epoch: [16] Total time: 0:01:40 (0.9645 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:29 model_time: 0.5039 (0.5039) evaluator_time: 0.0272 (0.0272) time: 1.1478 data: 0.5974 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4036 (0.4075) evaluator_time: 0.0105 (0.0197) time: 0.4528 data: 0.0203 max mem: 4355 Test: Total time: 0:00:12 (0.4817 s / it) Averaged stats: model_time: 0.4036 (0.4075) evaluator_time: 0.0105 (0.0197) Accumulating evaluation results... DONE (t=0.07s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.891 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.599 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577 Epoch: [17] [ 0/104] eta: 0:02:53 lr: 0.000000 loss: 0.2228 (0.2228) loss_classifier: 0.0683 (0.0683) loss_box_reg: 0.1485 (0.1485) loss_objectness: 0.0005 (0.0005) loss_rpn_box_reg: 0.0055 (0.0055) time: 1.6703 data: 0.6723 max mem: 4355 Epoch: [17] [ 10/104] eta: 0:01:37 lr: 0.000000 loss: 0.2259 (0.2446) loss_classifier: 0.0682 (0.0702) loss_box_reg: 0.1485 (0.1652) loss_objectness: 0.0024 (0.0025) loss_rpn_box_reg: 0.0051 (0.0067) time: 1.0412 data: 0.0821 max mem: 4355 Epoch: [17] [ 20/104] eta: 0:01:24 lr: 0.000000 loss: 0.2088 (0.2263) loss_classifier: 0.0600 (0.0636) loss_box_reg: 0.1369 (0.1543) loss_objectness: 0.0013 (0.0021) loss_rpn_box_reg: 0.0051 (0.0063) time: 0.9775 data: 0.0209 max mem: 4355 Epoch: [17] [ 30/104] eta: 0:01:13 lr: 0.000000 loss: 0.1915 (0.2150) loss_classifier: 0.0597 (0.0615) loss_box_reg: 0.1356 (0.1455) loss_objectness: 0.0010 (0.0021) loss_rpn_box_reg: 0.0037 (0.0060) time: 0.9745 data: 0.0200 max mem: 4355 Epoch: [17] [ 40/104] eta: 0:01:03 lr: 0.000000 loss: 0.1814 (0.2125) loss_classifier: 0.0545 (0.0611) loss_box_reg: 0.1115 (0.1434) loss_objectness: 0.0017 (0.0021) loss_rpn_box_reg: 0.0043 (0.0059) time: 0.9677 data: 0.0219 max mem: 4355 Epoch: [17] [ 50/104] eta: 0:00:53 lr: 0.000000 loss: 0.1886 (0.2064) loss_classifier: 0.0545 (0.0596) loss_box_reg: 0.1244 (0.1392) loss_objectness: 0.0015 (0.0020) loss_rpn_box_reg: 0.0045 (0.0057) time: 0.9561 data: 0.0217 max mem: 4355 Epoch: [17] [ 60/104] eta: 0:00:42 lr: 0.000000 loss: 0.1886 (0.2076) loss_classifier: 0.0568 (0.0593) loss_box_reg: 0.1260 (0.1406) loss_objectness: 0.0010 (0.0019) loss_rpn_box_reg: 0.0051 (0.0057) time: 0.9437 data: 0.0201 max mem: 4355 Epoch: [17] [ 70/104] eta: 0:00:33 lr: 0.000000 loss: 0.1979 (0.2084) loss_classifier: 0.0573 (0.0592) loss_box_reg: 0.1360 (0.1410) loss_objectness: 0.0009 (0.0019) loss_rpn_box_reg: 0.0052 (0.0062) time: 0.9444 data: 0.0211 max mem: 4355 Epoch: [17] [ 80/104] eta: 0:00:23 lr: 0.000000 loss: 0.2045 (0.2104) loss_classifier: 0.0634 (0.0599) loss_box_reg: 0.1410 (0.1421) loss_objectness: 0.0016 (0.0021) loss_rpn_box_reg: 0.0052 (0.0063) time: 0.9502 data: 0.0218 max mem: 4355 Epoch: [17] [ 90/104] eta: 0:00:13 lr: 0.000000 loss: 0.2384 (0.2129) loss_classifier: 0.0647 (0.0601) loss_box_reg: 0.1636 (0.1442) loss_objectness: 0.0020 (0.0021) loss_rpn_box_reg: 0.0074 (0.0065) time: 0.9483 data: 0.0199 max mem: 4355 Epoch: [17] [100/104] eta: 0:00:03 lr: 0.000000 loss: 0.2158 (0.2130) loss_classifier: 0.0586 (0.0607) loss_box_reg: 0.1437 (0.1438) loss_objectness: 0.0016 (0.0021) loss_rpn_box_reg: 0.0068 (0.0064) time: 0.9540 data: 0.0199 max mem: 4355 Epoch: [17] [103/104] eta: 0:00:00 lr: 0.000000 loss: 0.2158 (0.2133) loss_classifier: 0.0586 (0.0610) loss_box_reg: 0.1389 (0.1439) loss_objectness: 0.0013 (0.0020) loss_rpn_box_reg: 0.0068 (0.0064) time: 0.9535 data: 0.0194 max mem: 4355 Epoch: [17] Total time: 0:01:40 (0.9661 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:31 model_time: 0.4864 (0.4864) evaluator_time: 0.0164 (0.0164) time: 1.2006 data: 0.6887 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4035 (0.4075) evaluator_time: 0.0103 (0.0186) time: 0.4453 data: 0.0190 max mem: 4355 Test: Total time: 0:00:12 (0.4825 s / it) Averaged stats: model_time: 0.4035 (0.4075) evaluator_time: 0.0103 (0.0186) Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.891 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.599 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577 Epoch: [18] [ 0/104] eta: 0:02:44 lr: 0.000000 loss: 0.1811 (0.1811) loss_classifier: 0.0471 (0.0471) loss_box_reg: 0.1268 (0.1268) loss_objectness: 0.0014 (0.0014) loss_rpn_box_reg: 0.0057 (0.0057) time: 1.5804 data: 0.5799 max mem: 4355 Epoch: [18] [ 10/104] eta: 0:01:35 lr: 0.000000 loss: 0.1901 (0.2078) loss_classifier: 0.0491 (0.0559) loss_box_reg: 0.1355 (0.1444) loss_objectness: 0.0014 (0.0022) loss_rpn_box_reg: 0.0054 (0.0053) time: 1.0210 data: 0.0702 max mem: 4355 Epoch: [18] [ 20/104] eta: 0:01:24 lr: 0.000000 loss: 0.1901 (0.1995) loss_classifier: 0.0491 (0.0550) loss_box_reg: 0.1301 (0.1375) loss_objectness: 0.0010 (0.0016) loss_rpn_box_reg: 0.0051 (0.0054) time: 0.9762 data: 0.0203 max mem: 4355 Epoch: [18] [ 30/104] eta: 0:01:13 lr: 0.000000 loss: 0.2086 (0.2101) loss_classifier: 0.0564 (0.0594) loss_box_reg: 0.1423 (0.1421) loss_objectness: 0.0011 (0.0018) loss_rpn_box_reg: 0.0058 (0.0067) time: 0.9767 data: 0.0200 max mem: 4355 Epoch: [18] [ 40/104] eta: 0:01:03 lr: 0.000000 loss: 0.2099 (0.2119) loss_classifier: 0.0646 (0.0605) loss_box_reg: 0.1433 (0.1434) loss_objectness: 0.0010 (0.0016) loss_rpn_box_reg: 0.0058 (0.0065) time: 0.9644 data: 0.0201 max mem: 4355 Epoch: [18] [ 50/104] eta: 0:00:52 lr: 0.000000 loss: 0.2033 (0.2132) loss_classifier: 0.0574 (0.0613) loss_box_reg: 0.1390 (0.1438) loss_objectness: 0.0009 (0.0018) loss_rpn_box_reg: 0.0048 (0.0063) time: 0.9598 data: 0.0212 max mem: 4355 Epoch: [18] [ 60/104] eta: 0:00:42 lr: 0.000000 loss: 0.2164 (0.2134) loss_classifier: 0.0625 (0.0611) loss_box_reg: 0.1473 (0.1440) loss_objectness: 0.0015 (0.0019) loss_rpn_box_reg: 0.0052 (0.0064) time: 0.9509 data: 0.0198 max mem: 4355 Epoch: [18] [ 70/104] eta: 0:00:32 lr: 0.000000 loss: 0.2271 (0.2201) loss_classifier: 0.0684 (0.0633) loss_box_reg: 0.1520 (0.1478) loss_objectness: 0.0013 (0.0020) loss_rpn_box_reg: 0.0072 (0.0070) time: 0.9440 data: 0.0197 max mem: 4355 Epoch: [18] [ 80/104] eta: 0:00:23 lr: 0.000000 loss: 0.2077 (0.2164) loss_classifier: 0.0689 (0.0623) loss_box_reg: 0.1292 (0.1453) loss_objectness: 0.0010 (0.0020) loss_rpn_box_reg: 0.0062 (0.0067) time: 0.9465 data: 0.0217 max mem: 4355 Epoch: [18] [ 90/104] eta: 0:00:13 lr: 0.000000 loss: 0.1816 (0.2148) loss_classifier: 0.0550 (0.0620) loss_box_reg: 0.1271 (0.1443) loss_objectness: 0.0011 (0.0020) loss_rpn_box_reg: 0.0045 (0.0066) time: 0.9505 data: 0.0209 max mem: 4355 Epoch: [18] [100/104] eta: 0:00:03 lr: 0.000000 loss: 0.1953 (0.2134) loss_classifier: 0.0550 (0.0614) loss_box_reg: 0.1409 (0.1436) loss_objectness: 0.0017 (0.0020) loss_rpn_box_reg: 0.0050 (0.0064) time: 0.9477 data: 0.0182 max mem: 4355 Epoch: [18] [103/104] eta: 0:00:00 lr: 0.000000 loss: 0.1915 (0.2134) loss_classifier: 0.0503 (0.0613) loss_box_reg: 0.1400 (0.1437) loss_objectness: 0.0017 (0.0019) loss_rpn_box_reg: 0.0050 (0.0064) time: 0.9502 data: 0.0179 max mem: 4355 Epoch: [18] Total time: 0:01:40 (0.9646 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:44 model_time: 0.5206 (0.5206) evaluator_time: 0.0464 (0.0464) time: 1.6959 data: 1.0941 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4023 (0.4074) evaluator_time: 0.0100 (0.0169) time: 0.4413 data: 0.0183 max mem: 4355 Test: Total time: 0:00:12 (0.4958 s / it) Averaged stats: model_time: 0.4023 (0.4074) evaluator_time: 0.0100 (0.0169) Accumulating evaluation results... DONE (t=0.14s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.891 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.599 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577 Epoch: [19] [ 0/104] eta: 0:03:53 lr: 0.000000 loss: 0.1829 (0.1829) loss_classifier: 0.0515 (0.0515) loss_box_reg: 0.1262 (0.1262) loss_objectness: 0.0006 (0.0006) loss_rpn_box_reg: 0.0045 (0.0045) time: 2.2405 data: 1.0624 max mem: 4355 Epoch: [19] [ 10/104] eta: 0:01:41 lr: 0.000000 loss: 0.2071 (0.2052) loss_classifier: 0.0543 (0.0603) loss_box_reg: 0.1446 (0.1373) loss_objectness: 0.0013 (0.0020) loss_rpn_box_reg: 0.0041 (0.0056) time: 1.0810 data: 0.1122 max mem: 4355 Epoch: [19] [ 20/104] eta: 0:01:27 lr: 0.000000 loss: 0.2236 (0.2161) loss_classifier: 0.0623 (0.0630) loss_box_reg: 0.1482 (0.1448) loss_objectness: 0.0013 (0.0020) loss_rpn_box_reg: 0.0041 (0.0064) time: 0.9863 data: 0.0227 max mem: 4355 Epoch: [19] [ 30/104] eta: 0:01:15 lr: 0.000000 loss: 0.2097 (0.2103) loss_classifier: 0.0624 (0.0601) loss_box_reg: 0.1477 (0.1420) loss_objectness: 0.0012 (0.0021) loss_rpn_box_reg: 0.0043 (0.0061) time: 0.9885 data: 0.0240 max mem: 4355 Epoch: [19] [ 40/104] eta: 0:01:04 lr: 0.000000 loss: 0.1973 (0.2107) loss_classifier: 0.0545 (0.0597) loss_box_reg: 0.1385 (0.1430) loss_objectness: 0.0010 (0.0020) loss_rpn_box_reg: 0.0040 (0.0060) time: 0.9628 data: 0.0196 max mem: 4355 Epoch: [19] [ 50/104] eta: 0:00:53 lr: 0.000000 loss: 0.2070 (0.2117) loss_classifier: 0.0605 (0.0604) loss_box_reg: 0.1422 (0.1434) loss_objectness: 0.0011 (0.0020) loss_rpn_box_reg: 0.0048 (0.0059) time: 0.9559 data: 0.0208 max mem: 4355 Epoch: [19] [ 60/104] eta: 0:00:43 lr: 0.000000 loss: 0.2082 (0.2123) loss_classifier: 0.0610 (0.0605) loss_box_reg: 0.1472 (0.1438) loss_objectness: 0.0013 (0.0020) loss_rpn_box_reg: 0.0052 (0.0059) time: 0.9552 data: 0.0220 max mem: 4355 Epoch: [19] [ 70/104] eta: 0:00:33 lr: 0.000000 loss: 0.2055 (0.2094) loss_classifier: 0.0566 (0.0601) loss_box_reg: 0.1378 (0.1415) loss_objectness: 0.0013 (0.0020) loss_rpn_box_reg: 0.0051 (0.0058) time: 0.9512 data: 0.0225 max mem: 4355 Epoch: [19] [ 80/104] eta: 0:00:23 lr: 0.000000 loss: 0.1907 (0.2100) loss_classifier: 0.0542 (0.0603) loss_box_reg: 0.1284 (0.1417) loss_objectness: 0.0015 (0.0020) loss_rpn_box_reg: 0.0061 (0.0060) time: 0.9499 data: 0.0225 max mem: 4355 Epoch: [19] [ 90/104] eta: 0:00:13 lr: 0.000000 loss: 0.2205 (0.2123) loss_classifier: 0.0542 (0.0612) loss_box_reg: 0.1497 (0.1430) loss_objectness: 0.0016 (0.0020) loss_rpn_box_reg: 0.0061 (0.0061) time: 0.9531 data: 0.0213 max mem: 4355 Epoch: [19] [100/104] eta: 0:00:03 lr: 0.000000 loss: 0.2263 (0.2144) loss_classifier: 0.0658 (0.0623) loss_box_reg: 0.1501 (0.1435) loss_objectness: 0.0020 (0.0021) loss_rpn_box_reg: 0.0061 (0.0065) time: 0.9510 data: 0.0190 max mem: 4355 Epoch: [19] [103/104] eta: 0:00:00 lr: 0.000000 loss: 0.2239 (0.2140) loss_classifier: 0.0591 (0.0621) loss_box_reg: 0.1518 (0.1434) loss_objectness: 0.0020 (0.0021) loss_rpn_box_reg: 0.0061 (0.0064) time: 0.9546 data: 0.0190 max mem: 4355 Epoch: [19] Total time: 0:01:41 (0.9745 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:38 model_time: 0.4969 (0.4969) evaluator_time: 0.0347 (0.0347) time: 1.4771 data: 0.9138 max mem: 4355 Test: [25/26] eta: 0:00:00 model_time: 0.4027 (0.4067) evaluator_time: 0.0105 (0.0176) time: 0.4442 data: 0.0181 max mem: 4355 Test: Total time: 0:00:12 (0.4899 s / it) Averaged stats: model_time: 0.4027 (0.4067) evaluator_time: 0.0105 (0.0176) Accumulating evaluation results... DONE (t=0.14s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.891 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.599 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577
#save adamn
import pickle
Filename = "FRCNNsgd.pkl"
# Define the file path where you want to save the model
filename = "/content/drive/MyDrive/dataset/FRCNNsgd.pkl"
# Save the model to the specified file path
torch.save(model.state_dict(), filename)
# Save the Modle to file in the current working directory
with open(Filename, 'wb') as file:
pickle.dump(model, file)
# Load the Model back from file
with open(Filename, 'rb') as file:
model = pickle.load(file)
model
FasterRCNN(
(transform): GeneralizedRCNNTransform(
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
Resize(min_size=(800,), max_size=1333, mode='bilinear')
)
(backbone): BackboneWithFPN(
(body): IntermediateLayerGetter(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): FrozenBatchNorm2d(256, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(512, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(1024, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(2048, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
)
)
(fpn): FeaturePyramidNetwork(
(inner_blocks): ModuleList(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
)
(1): Conv2dNormActivation(
(0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
(2): Conv2dNormActivation(
(0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
)
(3): Conv2dNormActivation(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
)
)
(layer_blocks): ModuleList(
(0-3): 4 x Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(extra_blocks): LastLevelMaxPool()
)
)
(rpn): RegionProposalNetwork(
(anchor_generator): AnchorGenerator()
(head): RPNHead(
(conv): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
)
)
(cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
(bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
)
)
(roi_heads): RoIHeads(
(box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)
(box_head): TwoMLPHead(
(fc6): Linear(in_features=12544, out_features=1024, bias=True)
(fc7): Linear(in_features=1024, out_features=1024, bias=True)
)
(box_predictor): FastRCNNPredictor(
(cls_score): Linear(in_features=1024, out_features=11, bias=True)
(bbox_pred): Linear(in_features=1024, out_features=44, bias=True)
)
)
)
# to train on GPU if selected
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# number of classes
num_classes = 11
# get the model using our helper function
model = get_object_detection_model(num_classes)
# move model to the right device
model.to(device)
# construct an optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.Adam(params, lr=0.001, weight_decay=0.0005)
# and a learning rate scheduler which decreases the learning rate by
# 10x every 3 epochs
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=3,
gamma=0.1)
# training for 8 epochs # adam
num_epochs = 15
for epoch in range(num_epochs):
# training for one epoch
train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
# update the learning rate
lr_scheduler.step()
# evaluate on the test dataset
evaluate(model, data_loader_test, device=device)
Epoch: [0] [ 0/104] eta: 0:03:27 lr: 0.000011 loss: 3.4412 (3.4412) loss_classifier: 2.6871 (2.6871) loss_box_reg: 0.2420 (0.2420) loss_objectness: 0.4842 (0.4842) loss_rpn_box_reg: 0.0278 (0.0278) time: 1.9959 data: 0.8924 max mem: 6508 Epoch: [0] [ 10/104] eta: 0:01:51 lr: 0.000108 loss: 1.9666 (2.0528) loss_classifier: 1.1984 (1.5021) loss_box_reg: 0.3116 (0.3519) loss_objectness: 0.1556 (0.1792) loss_rpn_box_reg: 0.0162 (0.0196) time: 1.1900 data: 0.0974 max mem: 6508 Epoch: [0] [ 20/104] eta: 0:01:37 lr: 0.000205 loss: 1.0744 (1.5374) loss_classifier: 0.5573 (1.0176) loss_box_reg: 0.3418 (0.3596) loss_objectness: 0.1066 (0.1406) loss_rpn_box_reg: 0.0162 (0.0196) time: 1.1201 data: 0.0198 max mem: 6508 Epoch: [0] [ 30/104] eta: 0:01:23 lr: 0.000302 loss: 0.8417 (1.3277) loss_classifier: 0.4224 (0.8269) loss_box_reg: 0.3510 (0.3689) loss_objectness: 0.0588 (0.1136) loss_rpn_box_reg: 0.0132 (0.0184) time: 1.1032 data: 0.0203 max mem: 6508 Epoch: [0] [ 40/104] eta: 0:01:11 lr: 0.000399 loss: 0.8124 (1.2217) loss_classifier: 0.4127 (0.7357) loss_box_reg: 0.3445 (0.3706) loss_objectness: 0.0425 (0.0973) loss_rpn_box_reg: 0.0132 (0.0182) time: 1.0606 data: 0.0197 max mem: 6508 Epoch: [0] [ 50/104] eta: 0:00:59 lr: 0.000496 loss: 0.8505 (1.1649) loss_classifier: 0.4300 (0.6826) loss_box_reg: 0.3445 (0.3667) loss_objectness: 0.0415 (0.0961) loss_rpn_box_reg: 0.0172 (0.0196) time: 1.0428 data: 0.0225 max mem: 6508 Epoch: [0] [ 60/104] eta: 0:00:47 lr: 0.000593 loss: 0.9567 (1.1331) loss_classifier: 0.4300 (0.6512) loss_box_reg: 0.3505 (0.3687) loss_objectness: 0.0581 (0.0926) loss_rpn_box_reg: 0.0223 (0.0206) time: 1.0324 data: 0.0233 max mem: 6508 Epoch: [0] [ 70/104] eta: 0:00:36 lr: 0.000690 loss: 0.8243 (1.0775) loss_classifier: 0.3951 (0.6126) loss_box_reg: 0.2874 (0.3552) loss_objectness: 0.0613 (0.0892) loss_rpn_box_reg: 0.0214 (0.0204) time: 1.0229 data: 0.0206 max mem: 6508 Epoch: [0] [ 80/104] eta: 0:00:25 lr: 0.000787 loss: 0.8379 (1.0610) loss_classifier: 0.3951 (0.5918) loss_box_reg: 0.3641 (0.3622) loss_objectness: 0.0537 (0.0854) loss_rpn_box_reg: 0.0173 (0.0216) time: 1.0280 data: 0.0188 max mem: 6508 Epoch: [0] [ 90/104] eta: 0:00:14 lr: 0.000884 loss: 0.8269 (1.0324) loss_classifier: 0.3935 (0.5685) loss_box_reg: 0.3835 (0.3585) loss_objectness: 0.0555 (0.0838) loss_rpn_box_reg: 0.0216 (0.0215) time: 1.0435 data: 0.0205 max mem: 6508 Epoch: [0] [100/104] eta: 0:00:04 lr: 0.000981 loss: 0.7398 (1.0072) loss_classifier: 0.3464 (0.5445) loss_box_reg: 0.3031 (0.3547) loss_objectness: 0.0724 (0.0851) loss_rpn_box_reg: 0.0199 (0.0228) time: 1.0630 data: 0.0221 max mem: 6508 Epoch: [0] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.7319 (0.9981) loss_classifier: 0.3661 (0.5396) loss_box_reg: 0.2775 (0.3502) loss_objectness: 0.0815 (0.0854) loss_rpn_box_reg: 0.0199 (0.0229) time: 1.0630 data: 0.0218 max mem: 6508 Epoch: [0] Total time: 0:01:51 (1.0705 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.5127 (0.5127) evaluator_time: 0.0156 (0.0156) time: 1.2586 data: 0.7145 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4363 (0.4386) evaluator_time: 0.0207 (0.0235) time: 0.4886 data: 0.0211 max mem: 6508 Test: Total time: 0:00:13 (0.5200 s / it) Averaged stats: model_time: 0.4363 (0.4386) evaluator_time: 0.0207 (0.0235) Accumulating evaluation results... DONE (t=0.16s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.086 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.227 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.041 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.082 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.079 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.079 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.060 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.180 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.206 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.199 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.088 Epoch: [1] [ 0/104] eta: 0:03:21 lr: 0.001000 loss: 0.8187 (0.8187) loss_classifier: 0.3262 (0.3262) loss_box_reg: 0.3955 (0.3955) loss_objectness: 0.0776 (0.0776) loss_rpn_box_reg: 0.0195 (0.0195) time: 1.9329 data: 0.8546 max mem: 6508 Epoch: [1] [ 10/104] eta: 0:01:47 lr: 0.001000 loss: 0.6260 (0.6550) loss_classifier: 0.2835 (0.2900) loss_box_reg: 0.2487 (0.2620) loss_objectness: 0.0706 (0.0771) loss_rpn_box_reg: 0.0216 (0.0259) time: 1.1428 data: 0.0923 max mem: 6508 Epoch: [1] [ 20/104] eta: 0:01:33 lr: 0.001000 loss: 0.6369 (0.6688) loss_classifier: 0.2835 (0.2994) loss_box_reg: 0.2845 (0.2835) loss_objectness: 0.0516 (0.0622) loss_rpn_box_reg: 0.0199 (0.0237) time: 1.0669 data: 0.0174 max mem: 6508 Epoch: [1] [ 30/104] eta: 0:01:20 lr: 0.001000 loss: 0.6915 (0.6925) loss_classifier: 0.2926 (0.3079) loss_box_reg: 0.3091 (0.3023) loss_objectness: 0.0429 (0.0586) loss_rpn_box_reg: 0.0173 (0.0237) time: 1.0603 data: 0.0194 max mem: 6508 Epoch: [1] [ 40/104] eta: 0:01:09 lr: 0.001000 loss: 0.7575 (0.7170) loss_classifier: 0.2951 (0.3153) loss_box_reg: 0.3427 (0.3221) loss_objectness: 0.0464 (0.0553) loss_rpn_box_reg: 0.0196 (0.0243) time: 1.0481 data: 0.0203 max mem: 6508 Epoch: [1] [ 50/104] eta: 0:00:57 lr: 0.001000 loss: 0.7855 (0.7342) loss_classifier: 0.3421 (0.3251) loss_box_reg: 0.3934 (0.3338) loss_objectness: 0.0324 (0.0501) loss_rpn_box_reg: 0.0207 (0.0252) time: 1.0417 data: 0.0209 max mem: 6508 Epoch: [1] [ 60/104] eta: 0:00:46 lr: 0.001000 loss: 0.7067 (0.7303) loss_classifier: 0.3367 (0.3236) loss_box_reg: 0.3516 (0.3330) loss_objectness: 0.0316 (0.0498) loss_rpn_box_reg: 0.0179 (0.0239) time: 1.0339 data: 0.0210 max mem: 6508 Epoch: [1] [ 70/104] eta: 0:00:36 lr: 0.001000 loss: 0.6577 (0.7207) loss_classifier: 0.2653 (0.3187) loss_box_reg: 0.2943 (0.3288) loss_objectness: 0.0441 (0.0500) loss_rpn_box_reg: 0.0164 (0.0232) time: 1.0331 data: 0.0216 max mem: 6508 Epoch: [1] [ 80/104] eta: 0:00:25 lr: 0.001000 loss: 0.6020 (0.7042) loss_classifier: 0.2453 (0.3072) loss_box_reg: 0.2700 (0.3235) loss_objectness: 0.0439 (0.0500) loss_rpn_box_reg: 0.0216 (0.0235) time: 1.0369 data: 0.0223 max mem: 6508 Epoch: [1] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.5815 (0.6954) loss_classifier: 0.2143 (0.3037) loss_box_reg: 0.2780 (0.3215) loss_objectness: 0.0288 (0.0475) loss_rpn_box_reg: 0.0215 (0.0227) time: 1.0432 data: 0.0225 max mem: 6508 Epoch: [1] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.6848 (0.6985) loss_classifier: 0.2656 (0.3040) loss_box_reg: 0.3175 (0.3224) loss_objectness: 0.0325 (0.0491) loss_rpn_box_reg: 0.0176 (0.0231) time: 1.0425 data: 0.0211 max mem: 6508 Epoch: [1] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.5815 (0.6889) loss_classifier: 0.2348 (0.2999) loss_box_reg: 0.2664 (0.3178) loss_objectness: 0.0311 (0.0485) loss_rpn_box_reg: 0.0149 (0.0227) time: 1.0401 data: 0.0202 max mem: 6508 Epoch: [1] Total time: 0:01:49 (1.0551 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:34 model_time: 0.5233 (0.5233) evaluator_time: 0.0307 (0.0307) time: 1.3337 data: 0.7621 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4390 (0.4415) evaluator_time: 0.0232 (0.0258) time: 0.4913 data: 0.0191 max mem: 6508 Test: Total time: 0:00:13 (0.5284 s / it) Averaged stats: model_time: 0.4390 (0.4415) evaluator_time: 0.0232 (0.0258) Accumulating evaluation results... DONE (t=0.25s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.209 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.479 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.124 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.186 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.257 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.087 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.108 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.303 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.316 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.396 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.183 Epoch: [2] [ 0/104] eta: 0:03:09 lr: 0.001000 loss: 0.2370 (0.2370) loss_classifier: 0.0976 (0.0976) loss_box_reg: 0.1113 (0.1113) loss_objectness: 0.0123 (0.0123) loss_rpn_box_reg: 0.0158 (0.0158) time: 1.8269 data: 0.7389 max mem: 6508 Epoch: [2] [ 10/104] eta: 0:01:47 lr: 0.001000 loss: 0.5648 (0.5501) loss_classifier: 0.1963 (0.2145) loss_box_reg: 0.2968 (0.2883) loss_objectness: 0.0258 (0.0299) loss_rpn_box_reg: 0.0164 (0.0173) time: 1.1384 data: 0.0856 max mem: 6508 Epoch: [2] [ 20/104] eta: 0:01:32 lr: 0.001000 loss: 0.5648 (0.5797) loss_classifier: 0.2199 (0.2261) loss_box_reg: 0.3092 (0.3060) loss_objectness: 0.0264 (0.0316) loss_rpn_box_reg: 0.0157 (0.0161) time: 1.0709 data: 0.0207 max mem: 6508 Epoch: [2] [ 30/104] eta: 0:01:20 lr: 0.001000 loss: 0.5875 (0.5984) loss_classifier: 0.2595 (0.2481) loss_box_reg: 0.3092 (0.3002) loss_objectness: 0.0277 (0.0320) loss_rpn_box_reg: 0.0142 (0.0181) time: 1.0615 data: 0.0213 max mem: 6508 Epoch: [2] [ 40/104] eta: 0:01:08 lr: 0.001000 loss: 0.6245 (0.6103) loss_classifier: 0.3000 (0.2579) loss_box_reg: 0.3041 (0.3047) loss_objectness: 0.0231 (0.0300) loss_rpn_box_reg: 0.0142 (0.0176) time: 1.0455 data: 0.0207 max mem: 6508 Epoch: [2] [ 50/104] eta: 0:00:57 lr: 0.001000 loss: 0.6245 (0.6336) loss_classifier: 0.2793 (0.2761) loss_box_reg: 0.3083 (0.3066) loss_objectness: 0.0225 (0.0332) loss_rpn_box_reg: 0.0139 (0.0177) time: 1.0367 data: 0.0215 max mem: 6508 Epoch: [2] [ 60/104] eta: 0:00:46 lr: 0.001000 loss: 0.6124 (0.6221) loss_classifier: 0.2727 (0.2745) loss_box_reg: 0.2801 (0.2973) loss_objectness: 0.0352 (0.0331) loss_rpn_box_reg: 0.0139 (0.0173) time: 1.0268 data: 0.0214 max mem: 6508 Epoch: [2] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.6184 (0.6230) loss_classifier: 0.2489 (0.2697) loss_box_reg: 0.2801 (0.2991) loss_objectness: 0.0333 (0.0354) loss_rpn_box_reg: 0.0180 (0.0188) time: 1.0177 data: 0.0197 max mem: 6508 Epoch: [2] [ 80/104] eta: 0:00:25 lr: 0.001000 loss: 0.6771 (0.6347) loss_classifier: 0.2657 (0.2747) loss_box_reg: 0.3387 (0.3036) loss_objectness: 0.0374 (0.0368) loss_rpn_box_reg: 0.0227 (0.0196) time: 1.0247 data: 0.0205 max mem: 6508 Epoch: [2] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.6771 (0.6362) loss_classifier: 0.2679 (0.2732) loss_box_reg: 0.3083 (0.3045) loss_objectness: 0.0435 (0.0381) loss_rpn_box_reg: 0.0217 (0.0204) time: 1.0385 data: 0.0222 max mem: 6508 Epoch: [2] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.6658 (0.6392) loss_classifier: 0.2378 (0.2721) loss_box_reg: 0.3310 (0.3084) loss_objectness: 0.0319 (0.0376) loss_rpn_box_reg: 0.0217 (0.0211) time: 1.0424 data: 0.0209 max mem: 6508 Epoch: [2] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.6658 (0.6404) loss_classifier: 0.2378 (0.2741) loss_box_reg: 0.3307 (0.3077) loss_objectness: 0.0302 (0.0376) loss_rpn_box_reg: 0.0200 (0.0211) time: 1.0437 data: 0.0205 max mem: 6508 Epoch: [2] Total time: 0:01:49 (1.0507 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:34 model_time: 0.5209 (0.5209) evaluator_time: 0.0362 (0.0362) time: 1.3311 data: 0.7572 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4389 (0.4390) evaluator_time: 0.0220 (0.0398) time: 0.5097 data: 0.0200 max mem: 6508 Test: Total time: 0:00:14 (0.5397 s / it) Averaged stats: model_time: 0.4389 (0.4390) evaluator_time: 0.0220 (0.0398) Accumulating evaluation results... DONE (t=0.16s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.534 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.122 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.130 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.132 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.111 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.311 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.289 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.246 Epoch: [3] [ 0/104] eta: 0:03:08 lr: 0.000100 loss: 0.6804 (0.6804) loss_classifier: 0.2065 (0.2065) loss_box_reg: 0.3854 (0.3854) loss_objectness: 0.0661 (0.0661) loss_rpn_box_reg: 0.0224 (0.0224) time: 1.8167 data: 0.6963 max mem: 6508 Epoch: [3] [ 10/104] eta: 0:01:47 lr: 0.000100 loss: 0.6202 (0.5907) loss_classifier: 0.2539 (0.2473) loss_box_reg: 0.2812 (0.2938) loss_objectness: 0.0324 (0.0320) loss_rpn_box_reg: 0.0170 (0.0176) time: 1.1409 data: 0.0849 max mem: 6508 Epoch: [3] [ 20/104] eta: 0:01:33 lr: 0.000100 loss: 0.5724 (0.5738) loss_classifier: 0.2272 (0.2427) loss_box_reg: 0.2722 (0.2854) loss_objectness: 0.0196 (0.0267) loss_rpn_box_reg: 0.0166 (0.0190) time: 1.0747 data: 0.0220 max mem: 6508 Epoch: [3] [ 30/104] eta: 0:01:20 lr: 0.000100 loss: 0.5440 (0.5548) loss_classifier: 0.2248 (0.2326) loss_box_reg: 0.2523 (0.2793) loss_objectness: 0.0185 (0.0241) loss_rpn_box_reg: 0.0143 (0.0189) time: 1.0620 data: 0.0196 max mem: 6508 Epoch: [3] [ 40/104] eta: 0:01:08 lr: 0.000100 loss: 0.4058 (0.5266) loss_classifier: 0.1643 (0.2222) loss_box_reg: 0.2174 (0.2646) loss_objectness: 0.0146 (0.0218) loss_rpn_box_reg: 0.0138 (0.0180) time: 1.0424 data: 0.0191 max mem: 6508 Epoch: [3] [ 50/104] eta: 0:00:57 lr: 0.000100 loss: 0.4355 (0.5124) loss_classifier: 0.1803 (0.2144) loss_box_reg: 0.2227 (0.2604) loss_objectness: 0.0157 (0.0206) loss_rpn_box_reg: 0.0144 (0.0171) time: 1.0329 data: 0.0197 max mem: 6508 Epoch: [3] [ 60/104] eta: 0:00:46 lr: 0.000100 loss: 0.4576 (0.5058) loss_classifier: 0.1871 (0.2110) loss_box_reg: 0.2408 (0.2578) loss_objectness: 0.0167 (0.0200) loss_rpn_box_reg: 0.0136 (0.0169) time: 1.0261 data: 0.0210 max mem: 6508 Epoch: [3] [ 70/104] eta: 0:00:35 lr: 0.000100 loss: 0.4142 (0.4890) loss_classifier: 0.1623 (0.2011) loss_box_reg: 0.2172 (0.2521) loss_objectness: 0.0118 (0.0188) loss_rpn_box_reg: 0.0135 (0.0169) time: 1.0213 data: 0.0208 max mem: 6508 Epoch: [3] [ 80/104] eta: 0:00:25 lr: 0.000100 loss: 0.3602 (0.4781) loss_classifier: 0.1426 (0.1944) loss_box_reg: 0.2080 (0.2488) loss_objectness: 0.0113 (0.0181) loss_rpn_box_reg: 0.0128 (0.0168) time: 1.0247 data: 0.0192 max mem: 6508 Epoch: [3] [ 90/104] eta: 0:00:14 lr: 0.000100 loss: 0.3589 (0.4657) loss_classifier: 0.1411 (0.1882) loss_box_reg: 0.2026 (0.2441) loss_objectness: 0.0113 (0.0174) loss_rpn_box_reg: 0.0097 (0.0161) time: 1.0343 data: 0.0197 max mem: 6508 Epoch: [3] [100/104] eta: 0:00:04 lr: 0.000100 loss: 0.3456 (0.4548) loss_classifier: 0.1264 (0.1822) loss_box_reg: 0.2009 (0.2405) loss_objectness: 0.0081 (0.0166) loss_rpn_box_reg: 0.0088 (0.0155) time: 1.0395 data: 0.0200 max mem: 6508 Epoch: [3] [103/104] eta: 0:00:01 lr: 0.000100 loss: 0.3425 (0.4524) loss_classifier: 0.1245 (0.1807) loss_box_reg: 0.1985 (0.2399) loss_objectness: 0.0081 (0.0163) loss_rpn_box_reg: 0.0088 (0.0154) time: 1.0407 data: 0.0198 max mem: 6508 Epoch: [3] Total time: 0:01:49 (1.0501 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:34 model_time: 0.4908 (0.4908) evaluator_time: 0.0537 (0.0537) time: 1.3125 data: 0.7402 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4388 (0.4388) evaluator_time: 0.0189 (0.0237) time: 0.4892 data: 0.0209 max mem: 6508 Test: Total time: 0:00:13 (0.5227 s / it) Averaged stats: model_time: 0.4388 (0.4388) evaluator_time: 0.0189 (0.0237) Accumulating evaluation results... DONE (t=0.15s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.408 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.733 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.421 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.316 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.459 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.320 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.176 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.483 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.545 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.438 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.588 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.441 Epoch: [4] [ 0/104] eta: 0:03:25 lr: 0.000100 loss: 0.5567 (0.5567) loss_classifier: 0.2793 (0.2793) loss_box_reg: 0.2358 (0.2358) loss_objectness: 0.0143 (0.0143) loss_rpn_box_reg: 0.0273 (0.0273) time: 1.9788 data: 0.8958 max mem: 6508 Epoch: [4] [ 10/104] eta: 0:01:47 lr: 0.000100 loss: 0.3472 (0.3862) loss_classifier: 0.1236 (0.1439) loss_box_reg: 0.2126 (0.2210) loss_objectness: 0.0074 (0.0085) loss_rpn_box_reg: 0.0107 (0.0127) time: 1.1458 data: 0.0970 max mem: 6508 Epoch: [4] [ 20/104] eta: 0:01:33 lr: 0.000100 loss: 0.3464 (0.3679) loss_classifier: 0.1226 (0.1420) loss_box_reg: 0.2008 (0.2060) loss_objectness: 0.0074 (0.0083) loss_rpn_box_reg: 0.0090 (0.0116) time: 1.0733 data: 0.0188 max mem: 6508 Epoch: [4] [ 30/104] eta: 0:01:21 lr: 0.000100 loss: 0.3354 (0.3654) loss_classifier: 0.1258 (0.1417) loss_box_reg: 0.1787 (0.2014) loss_objectness: 0.0095 (0.0106) loss_rpn_box_reg: 0.0081 (0.0116) time: 1.0694 data: 0.0209 max mem: 6508 Epoch: [4] [ 40/104] eta: 0:01:09 lr: 0.000100 loss: 0.3787 (0.3697) loss_classifier: 0.1354 (0.1431) loss_box_reg: 0.2111 (0.2041) loss_objectness: 0.0086 (0.0102) loss_rpn_box_reg: 0.0104 (0.0123) time: 1.0458 data: 0.0206 max mem: 6508 Epoch: [4] [ 50/104] eta: 0:00:57 lr: 0.000100 loss: 0.3625 (0.3595) loss_classifier: 0.1303 (0.1383) loss_box_reg: 0.2021 (0.1997) loss_objectness: 0.0069 (0.0097) loss_rpn_box_reg: 0.0091 (0.0119) time: 1.0313 data: 0.0203 max mem: 6508 Epoch: [4] [ 60/104] eta: 0:00:46 lr: 0.000100 loss: 0.3580 (0.3681) loss_classifier: 0.1295 (0.1408) loss_box_reg: 0.2104 (0.2048) loss_objectness: 0.0088 (0.0104) loss_rpn_box_reg: 0.0093 (0.0122) time: 1.0259 data: 0.0217 max mem: 6508 Epoch: [4] [ 70/104] eta: 0:00:35 lr: 0.000100 loss: 0.4037 (0.3660) loss_classifier: 0.1394 (0.1393) loss_box_reg: 0.2228 (0.2046) loss_objectness: 0.0093 (0.0102) loss_rpn_box_reg: 0.0093 (0.0119) time: 1.0230 data: 0.0207 max mem: 6508 Epoch: [4] [ 80/104] eta: 0:00:25 lr: 0.000100 loss: 0.3903 (0.3696) loss_classifier: 0.1387 (0.1403) loss_box_reg: 0.2209 (0.2077) loss_objectness: 0.0059 (0.0098) loss_rpn_box_reg: 0.0095 (0.0119) time: 1.0291 data: 0.0199 max mem: 6508 Epoch: [4] [ 90/104] eta: 0:00:14 lr: 0.000100 loss: 0.3678 (0.3685) loss_classifier: 0.1358 (0.1386) loss_box_reg: 0.2143 (0.2082) loss_objectness: 0.0059 (0.0097) loss_rpn_box_reg: 0.0107 (0.0120) time: 1.0391 data: 0.0204 max mem: 6508 Epoch: [4] [100/104] eta: 0:00:04 lr: 0.000100 loss: 0.3634 (0.3687) loss_classifier: 0.1216 (0.1377) loss_box_reg: 0.2001 (0.2093) loss_objectness: 0.0059 (0.0098) loss_rpn_box_reg: 0.0103 (0.0120) time: 1.0395 data: 0.0189 max mem: 6508 Epoch: [4] [103/104] eta: 0:00:01 lr: 0.000100 loss: 0.3103 (0.3659) loss_classifier: 0.1062 (0.1370) loss_box_reg: 0.1927 (0.2073) loss_objectness: 0.0061 (0.0099) loss_rpn_box_reg: 0.0099 (0.0118) time: 1.0396 data: 0.0185 max mem: 6508 Epoch: [4] Total time: 0:01:49 (1.0528 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.5330 (0.5330) evaluator_time: 0.0306 (0.0306) time: 1.2520 data: 0.6645 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4393 (0.4412) evaluator_time: 0.0190 (0.0213) time: 0.4881 data: 0.0204 max mem: 6508 Test: Total time: 0:00:13 (0.5197 s / it) Averaged stats: model_time: 0.4393 (0.4412) evaluator_time: 0.0190 (0.0213) Accumulating evaluation results... DONE (t=0.13s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.429 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.779 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.429 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.328 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.484 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.193 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.492 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.552 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.422 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.599 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487 Epoch: [5] [ 0/104] eta: 0:03:07 lr: 0.000100 loss: 0.4193 (0.4193) loss_classifier: 0.1600 (0.1600) loss_box_reg: 0.2258 (0.2258) loss_objectness: 0.0096 (0.0096) loss_rpn_box_reg: 0.0240 (0.0240) time: 1.8027 data: 0.7181 max mem: 6508 Epoch: [5] [ 10/104] eta: 0:01:46 lr: 0.000100 loss: 0.3269 (0.3370) loss_classifier: 0.1390 (0.1268) loss_box_reg: 0.1936 (0.1918) loss_objectness: 0.0089 (0.0075) loss_rpn_box_reg: 0.0080 (0.0109) time: 1.1342 data: 0.0830 max mem: 6508 Epoch: [5] [ 20/104] eta: 0:01:33 lr: 0.000100 loss: 0.3544 (0.3734) loss_classifier: 0.1277 (0.1388) loss_box_reg: 0.2139 (0.2136) loss_objectness: 0.0081 (0.0082) loss_rpn_box_reg: 0.0104 (0.0128) time: 1.0746 data: 0.0207 max mem: 6508 Epoch: [5] [ 30/104] eta: 0:01:20 lr: 0.000100 loss: 0.3695 (0.3627) loss_classifier: 0.1277 (0.1345) loss_box_reg: 0.2237 (0.2076) loss_objectness: 0.0066 (0.0083) loss_rpn_box_reg: 0.0114 (0.0123) time: 1.0683 data: 0.0210 max mem: 6508 Epoch: [5] [ 40/104] eta: 0:01:09 lr: 0.000100 loss: 0.3503 (0.3576) loss_classifier: 0.1212 (0.1295) loss_box_reg: 0.2038 (0.2083) loss_objectness: 0.0055 (0.0077) loss_rpn_box_reg: 0.0103 (0.0121) time: 1.0502 data: 0.0212 max mem: 6508 Epoch: [5] [ 50/104] eta: 0:00:57 lr: 0.000100 loss: 0.3338 (0.3440) loss_classifier: 0.1079 (0.1239) loss_box_reg: 0.1861 (0.2002) loss_objectness: 0.0060 (0.0081) loss_rpn_box_reg: 0.0099 (0.0118) time: 1.0404 data: 0.0220 max mem: 6508 Epoch: [5] [ 60/104] eta: 0:00:46 lr: 0.000100 loss: 0.3008 (0.3420) loss_classifier: 0.1079 (0.1222) loss_box_reg: 0.1812 (0.2002) loss_objectness: 0.0064 (0.0077) loss_rpn_box_reg: 0.0101 (0.0118) time: 1.0358 data: 0.0226 max mem: 6508 Epoch: [5] [ 70/104] eta: 0:00:36 lr: 0.000100 loss: 0.3433 (0.3416) loss_classifier: 0.1082 (0.1228) loss_box_reg: 0.1927 (0.2000) loss_objectness: 0.0056 (0.0077) loss_rpn_box_reg: 0.0085 (0.0111) time: 1.0265 data: 0.0210 max mem: 6508 Epoch: [5] [ 80/104] eta: 0:00:25 lr: 0.000100 loss: 0.3123 (0.3360) loss_classifier: 0.1012 (0.1207) loss_box_reg: 0.1895 (0.1971) loss_objectness: 0.0042 (0.0074) loss_rpn_box_reg: 0.0065 (0.0108) time: 1.0265 data: 0.0205 max mem: 6508 Epoch: [5] [ 90/104] eta: 0:00:14 lr: 0.000100 loss: 0.2546 (0.3282) loss_classifier: 0.0837 (0.1173) loss_box_reg: 0.1607 (0.1930) loss_objectness: 0.0051 (0.0073) loss_rpn_box_reg: 0.0070 (0.0105) time: 1.0385 data: 0.0220 max mem: 6508 Epoch: [5] [100/104] eta: 0:00:04 lr: 0.000100 loss: 0.2826 (0.3304) loss_classifier: 0.0950 (0.1175) loss_box_reg: 0.1764 (0.1952) loss_objectness: 0.0046 (0.0070) loss_rpn_box_reg: 0.0077 (0.0108) time: 1.0391 data: 0.0206 max mem: 6508 Epoch: [5] [103/104] eta: 0:00:01 lr: 0.000100 loss: 0.2938 (0.3298) loss_classifier: 0.1015 (0.1174) loss_box_reg: 0.1781 (0.1946) loss_objectness: 0.0046 (0.0071) loss_rpn_box_reg: 0.0076 (0.0107) time: 1.0391 data: 0.0204 max mem: 6508 Epoch: [5] Total time: 0:01:49 (1.0535 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:34 model_time: 0.4812 (0.4812) evaluator_time: 0.0558 (0.0558) time: 1.3086 data: 0.7417 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4412 (0.4418) evaluator_time: 0.0157 (0.0197) time: 0.4870 data: 0.0209 max mem: 6508 Test: Total time: 0:00:13 (0.5221 s / it) Averaged stats: model_time: 0.4412 (0.4418) evaluator_time: 0.0157 (0.0197) Accumulating evaluation results... DONE (t=0.12s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.452 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.800 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.448 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.337 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.517 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.327 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.192 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.501 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.558 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.448 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.606 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.470 Epoch: [6] [ 0/104] eta: 0:03:33 lr: 0.000010 loss: 0.4073 (0.4073) loss_classifier: 0.1331 (0.1331) loss_box_reg: 0.2401 (0.2401) loss_objectness: 0.0098 (0.0098) loss_rpn_box_reg: 0.0243 (0.0243) time: 2.0575 data: 0.9893 max mem: 6508 Epoch: [6] [ 10/104] eta: 0:01:48 lr: 0.000010 loss: 0.2835 (0.3123) loss_classifier: 0.1123 (0.1112) loss_box_reg: 0.1738 (0.1859) loss_objectness: 0.0055 (0.0062) loss_rpn_box_reg: 0.0080 (0.0090) time: 1.1551 data: 0.1058 max mem: 6508 Epoch: [6] [ 20/104] eta: 0:01:33 lr: 0.000010 loss: 0.2868 (0.3128) loss_classifier: 0.1066 (0.1121) loss_box_reg: 0.1738 (0.1840) loss_objectness: 0.0049 (0.0067) loss_rpn_box_reg: 0.0081 (0.0100) time: 1.0704 data: 0.0195 max mem: 6508 Epoch: [6] [ 30/104] eta: 0:01:21 lr: 0.000010 loss: 0.2868 (0.3018) loss_classifier: 0.0995 (0.1087) loss_box_reg: 0.1600 (0.1769) loss_objectness: 0.0046 (0.0066) loss_rpn_box_reg: 0.0082 (0.0095) time: 1.0642 data: 0.0202 max mem: 6508 Epoch: [6] [ 40/104] eta: 0:01:09 lr: 0.000010 loss: 0.2755 (0.3013) loss_classifier: 0.0995 (0.1096) loss_box_reg: 0.1557 (0.1758) loss_objectness: 0.0041 (0.0062) loss_rpn_box_reg: 0.0082 (0.0098) time: 1.0481 data: 0.0203 max mem: 6508 Epoch: [6] [ 50/104] eta: 0:00:58 lr: 0.000010 loss: 0.2693 (0.2903) loss_classifier: 0.0950 (0.1054) loss_box_reg: 0.1473 (0.1697) loss_objectness: 0.0033 (0.0057) loss_rpn_box_reg: 0.0079 (0.0095) time: 1.0435 data: 0.0249 max mem: 6508 Epoch: [6] [ 60/104] eta: 0:00:46 lr: 0.000010 loss: 0.2315 (0.2823) loss_classifier: 0.0860 (0.1030) loss_box_reg: 0.1282 (0.1646) loss_objectness: 0.0033 (0.0055) loss_rpn_box_reg: 0.0067 (0.0093) time: 1.0364 data: 0.0246 max mem: 6508 Epoch: [6] [ 70/104] eta: 0:00:36 lr: 0.000010 loss: 0.2315 (0.2804) loss_classifier: 0.0908 (0.1022) loss_box_reg: 0.1212 (0.1636) loss_objectness: 0.0036 (0.0052) loss_rpn_box_reg: 0.0056 (0.0094) time: 1.0283 data: 0.0202 max mem: 6508 Epoch: [6] [ 80/104] eta: 0:00:25 lr: 0.000010 loss: 0.2799 (0.2840) loss_classifier: 0.0908 (0.1025) loss_box_reg: 0.1810 (0.1670) loss_objectness: 0.0036 (0.0052) loss_rpn_box_reg: 0.0075 (0.0092) time: 1.0330 data: 0.0210 max mem: 6508 Epoch: [6] [ 90/104] eta: 0:00:14 lr: 0.000010 loss: 0.2819 (0.2843) loss_classifier: 0.1034 (0.1022) loss_box_reg: 0.1826 (0.1673) loss_objectness: 0.0040 (0.0053) loss_rpn_box_reg: 0.0078 (0.0094) time: 1.0453 data: 0.0224 max mem: 6508 Epoch: [6] [100/104] eta: 0:00:04 lr: 0.000010 loss: 0.2860 (0.2871) loss_classifier: 0.1034 (0.1026) loss_box_reg: 0.1680 (0.1699) loss_objectness: 0.0045 (0.0052) loss_rpn_box_reg: 0.0069 (0.0094) time: 1.0449 data: 0.0208 max mem: 6508 Epoch: [6] [103/104] eta: 0:00:01 lr: 0.000010 loss: 0.2860 (0.2863) loss_classifier: 0.1024 (0.1020) loss_box_reg: 0.1680 (0.1698) loss_objectness: 0.0034 (0.0052) loss_rpn_box_reg: 0.0085 (0.0093) time: 1.0419 data: 0.0200 max mem: 6508 Epoch: [6] Total time: 0:01:49 (1.0573 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:33 model_time: 0.5281 (0.5281) evaluator_time: 0.0275 (0.0275) time: 1.2963 data: 0.7170 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4374 (0.4399) evaluator_time: 0.0158 (0.0183) time: 0.4828 data: 0.0199 max mem: 6508 Test: Total time: 0:00:13 (0.5165 s / it) Averaged stats: model_time: 0.4374 (0.4399) evaluator_time: 0.0158 (0.0183) Accumulating evaluation results... DONE (t=0.11s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.478 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.821 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.482 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.332 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.367 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.208 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.518 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.576 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.444 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.623 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.492 Epoch: [7] [ 0/104] eta: 0:03:22 lr: 0.000010 loss: 0.4399 (0.4399) loss_classifier: 0.1619 (0.1619) loss_box_reg: 0.2502 (0.2502) loss_objectness: 0.0036 (0.0036) loss_rpn_box_reg: 0.0242 (0.0242) time: 1.9476 data: 0.8770 max mem: 6508 Epoch: [7] [ 10/104] eta: 0:01:47 lr: 0.000010 loss: 0.2756 (0.2936) loss_classifier: 0.1017 (0.1059) loss_box_reg: 0.1496 (0.1697) loss_objectness: 0.0052 (0.0068) loss_rpn_box_reg: 0.0107 (0.0112) time: 1.1428 data: 0.0945 max mem: 6508 Epoch: [7] [ 20/104] eta: 0:01:33 lr: 0.000010 loss: 0.2791 (0.2945) loss_classifier: 0.1017 (0.1067) loss_box_reg: 0.1614 (0.1703) loss_objectness: 0.0047 (0.0072) loss_rpn_box_reg: 0.0082 (0.0103) time: 1.0662 data: 0.0175 max mem: 6508 Epoch: [7] [ 30/104] eta: 0:01:20 lr: 0.000010 loss: 0.2799 (0.2795) loss_classifier: 0.0968 (0.1006) loss_box_reg: 0.1614 (0.1632) loss_objectness: 0.0030 (0.0059) loss_rpn_box_reg: 0.0076 (0.0098) time: 1.0604 data: 0.0194 max mem: 6508 Epoch: [7] [ 40/104] eta: 0:01:09 lr: 0.000010 loss: 0.2400 (0.2744) loss_classifier: 0.0840 (0.0997) loss_box_reg: 0.1456 (0.1602) loss_objectness: 0.0030 (0.0055) loss_rpn_box_reg: 0.0067 (0.0090) time: 1.0469 data: 0.0205 max mem: 6508 Epoch: [7] [ 50/104] eta: 0:00:57 lr: 0.000010 loss: 0.2466 (0.2673) loss_classifier: 0.0838 (0.0960) loss_box_reg: 0.1572 (0.1577) loss_objectness: 0.0029 (0.0051) loss_rpn_box_reg: 0.0061 (0.0085) time: 1.0376 data: 0.0209 max mem: 6508 Epoch: [7] [ 60/104] eta: 0:00:46 lr: 0.000010 loss: 0.2689 (0.2686) loss_classifier: 0.0875 (0.0953) loss_box_reg: 0.1658 (0.1593) loss_objectness: 0.0025 (0.0052) loss_rpn_box_reg: 0.0078 (0.0088) time: 1.0300 data: 0.0211 max mem: 6508 Epoch: [7] [ 70/104] eta: 0:00:35 lr: 0.000010 loss: 0.2628 (0.2667) loss_classifier: 0.0850 (0.0944) loss_box_reg: 0.1587 (0.1587) loss_objectness: 0.0030 (0.0049) loss_rpn_box_reg: 0.0078 (0.0087) time: 1.0244 data: 0.0200 max mem: 6508 Epoch: [7] [ 80/104] eta: 0:00:25 lr: 0.000010 loss: 0.2451 (0.2705) loss_classifier: 0.0906 (0.0968) loss_box_reg: 0.1557 (0.1600) loss_objectness: 0.0023 (0.0048) loss_rpn_box_reg: 0.0069 (0.0090) time: 1.0308 data: 0.0205 max mem: 6508 Epoch: [7] [ 90/104] eta: 0:00:14 lr: 0.000010 loss: 0.2572 (0.2687) loss_classifier: 0.0913 (0.0959) loss_box_reg: 0.1540 (0.1592) loss_objectness: 0.0025 (0.0046) loss_rpn_box_reg: 0.0075 (0.0090) time: 1.0387 data: 0.0218 max mem: 6508 Epoch: [7] [100/104] eta: 0:00:04 lr: 0.000010 loss: 0.2582 (0.2735) loss_classifier: 0.0950 (0.0978) loss_box_reg: 0.1645 (0.1620) loss_objectness: 0.0034 (0.0047) loss_rpn_box_reg: 0.0078 (0.0091) time: 1.0367 data: 0.0199 max mem: 6508 Epoch: [7] [103/104] eta: 0:00:01 lr: 0.000010 loss: 0.2582 (0.2739) loss_classifier: 0.1008 (0.0976) loss_box_reg: 0.1679 (0.1626) loss_objectness: 0.0030 (0.0047) loss_rpn_box_reg: 0.0075 (0.0090) time: 1.0379 data: 0.0195 max mem: 6508 Epoch: [7] Total time: 0:01:49 (1.0520 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.4987 (0.4987) evaluator_time: 0.0271 (0.0271) time: 1.2580 data: 0.7105 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4386 (0.4406) evaluator_time: 0.0146 (0.0174) time: 0.4851 data: 0.0209 max mem: 6508 Test: Total time: 0:00:13 (0.5171 s / it) Averaged stats: model_time: 0.4386 (0.4406) evaluator_time: 0.0146 (0.0174) Accumulating evaluation results... DONE (t=0.13s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.482 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.826 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.506 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.344 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.549 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.373 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.518 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.576 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.444 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.630 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.513 Epoch: [8] [ 0/104] eta: 0:03:25 lr: 0.000010 loss: 0.3998 (0.3998) loss_classifier: 0.1506 (0.1506) loss_box_reg: 0.2245 (0.2245) loss_objectness: 0.0096 (0.0096) loss_rpn_box_reg: 0.0151 (0.0151) time: 1.9714 data: 0.8839 max mem: 6508 Epoch: [8] [ 10/104] eta: 0:01:48 lr: 0.000010 loss: 0.3032 (0.2977) loss_classifier: 0.1042 (0.1058) loss_box_reg: 0.1803 (0.1755) loss_objectness: 0.0038 (0.0049) loss_rpn_box_reg: 0.0130 (0.0116) time: 1.1560 data: 0.0993 max mem: 6508 Epoch: [8] [ 20/104] eta: 0:01:33 lr: 0.000010 loss: 0.2519 (0.2779) loss_classifier: 0.0926 (0.0998) loss_box_reg: 0.1590 (0.1635) loss_objectness: 0.0033 (0.0044) loss_rpn_box_reg: 0.0080 (0.0103) time: 1.0757 data: 0.0202 max mem: 6508 Epoch: [8] [ 30/104] eta: 0:01:21 lr: 0.000010 loss: 0.2395 (0.2733) loss_classifier: 0.0878 (0.0978) loss_box_reg: 0.1522 (0.1611) loss_objectness: 0.0033 (0.0044) loss_rpn_box_reg: 0.0070 (0.0100) time: 1.0648 data: 0.0192 max mem: 6508 Epoch: [8] [ 40/104] eta: 0:01:09 lr: 0.000010 loss: 0.2420 (0.2735) loss_classifier: 0.0923 (0.0981) loss_box_reg: 0.1517 (0.1608) loss_objectness: 0.0044 (0.0047) loss_rpn_box_reg: 0.0070 (0.0098) time: 1.0438 data: 0.0193 max mem: 6508 Epoch: [8] [ 50/104] eta: 0:00:57 lr: 0.000010 loss: 0.2497 (0.2721) loss_classifier: 0.0996 (0.0978) loss_box_reg: 0.1467 (0.1600) loss_objectness: 0.0039 (0.0049) loss_rpn_box_reg: 0.0070 (0.0094) time: 1.0306 data: 0.0204 max mem: 6508 Epoch: [8] [ 60/104] eta: 0:00:46 lr: 0.000010 loss: 0.2640 (0.2726) loss_classifier: 0.0942 (0.0977) loss_box_reg: 0.1487 (0.1609) loss_objectness: 0.0035 (0.0047) loss_rpn_box_reg: 0.0069 (0.0093) time: 1.0261 data: 0.0208 max mem: 6508 Epoch: [8] [ 70/104] eta: 0:00:36 lr: 0.000010 loss: 0.2567 (0.2712) loss_classifier: 0.0847 (0.0970) loss_box_reg: 0.1487 (0.1604) loss_objectness: 0.0038 (0.0046) loss_rpn_box_reg: 0.0069 (0.0092) time: 1.0291 data: 0.0220 max mem: 6508 Epoch: [8] [ 80/104] eta: 0:00:25 lr: 0.000010 loss: 0.2421 (0.2696) loss_classifier: 0.0903 (0.0965) loss_box_reg: 0.1393 (0.1595) loss_objectness: 0.0039 (0.0046) loss_rpn_box_reg: 0.0065 (0.0090) time: 1.0323 data: 0.0223 max mem: 6508 Epoch: [8] [ 90/104] eta: 0:00:14 lr: 0.000010 loss: 0.2462 (0.2707) loss_classifier: 0.0924 (0.0966) loss_box_reg: 0.1409 (0.1606) loss_objectness: 0.0037 (0.0045) loss_rpn_box_reg: 0.0062 (0.0090) time: 1.0354 data: 0.0213 max mem: 6508 Epoch: [8] [100/104] eta: 0:00:04 lr: 0.000010 loss: 0.2585 (0.2689) loss_classifier: 0.0851 (0.0954) loss_box_reg: 0.1527 (0.1602) loss_objectness: 0.0033 (0.0044) loss_rpn_box_reg: 0.0065 (0.0088) time: 1.0377 data: 0.0201 max mem: 6508 Epoch: [8] [103/104] eta: 0:00:01 lr: 0.000010 loss: 0.2463 (0.2687) loss_classifier: 0.0792 (0.0952) loss_box_reg: 0.1509 (0.1602) loss_objectness: 0.0033 (0.0044) loss_rpn_box_reg: 0.0062 (0.0089) time: 1.0379 data: 0.0197 max mem: 6508 Epoch: [8] Total time: 0:01:49 (1.0532 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.5196 (0.5196) evaluator_time: 0.0677 (0.0677) time: 1.2434 data: 0.6343 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4391 (0.4423) evaluator_time: 0.0157 (0.0205) time: 0.4859 data: 0.0210 max mem: 6508 Test: Total time: 0:00:13 (0.5185 s / it) Averaged stats: model_time: 0.4391 (0.4423) evaluator_time: 0.0157 (0.0205) Accumulating evaluation results... DONE (t=0.10s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.483 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.828 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.492 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.335 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.545 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.386 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.522 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.576 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.445 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.629 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.528 Epoch: [9] [ 0/104] eta: 0:03:23 lr: 0.000001 loss: 0.1893 (0.1893) loss_classifier: 0.0680 (0.0680) loss_box_reg: 0.1151 (0.1151) loss_objectness: 0.0013 (0.0013) loss_rpn_box_reg: 0.0050 (0.0050) time: 1.9530 data: 0.8919 max mem: 6508 Epoch: [9] [ 10/104] eta: 0:01:48 lr: 0.000001 loss: 0.2888 (0.2850) loss_classifier: 0.1062 (0.1099) loss_box_reg: 0.1607 (0.1611) loss_objectness: 0.0035 (0.0051) loss_rpn_box_reg: 0.0061 (0.0089) time: 1.1501 data: 0.0951 max mem: 6508 Epoch: [9] [ 20/104] eta: 0:01:33 lr: 0.000001 loss: 0.2615 (0.2629) loss_classifier: 0.0999 (0.0993) loss_box_reg: 0.1388 (0.1513) loss_objectness: 0.0040 (0.0046) loss_rpn_box_reg: 0.0060 (0.0077) time: 1.0771 data: 0.0198 max mem: 6508 Epoch: [9] [ 30/104] eta: 0:01:21 lr: 0.000001 loss: 0.2615 (0.2642) loss_classifier: 0.0915 (0.0972) loss_box_reg: 0.1451 (0.1539) loss_objectness: 0.0042 (0.0046) loss_rpn_box_reg: 0.0081 (0.0085) time: 1.0698 data: 0.0227 max mem: 6508 Epoch: [9] [ 40/104] eta: 0:01:09 lr: 0.000001 loss: 0.2488 (0.2595) loss_classifier: 0.0905 (0.0954) loss_box_reg: 0.1451 (0.1510) loss_objectness: 0.0039 (0.0045) loss_rpn_box_reg: 0.0103 (0.0086) time: 1.0462 data: 0.0204 max mem: 6508 Epoch: [9] [ 50/104] eta: 0:00:58 lr: 0.000001 loss: 0.2303 (0.2553) loss_classifier: 0.0789 (0.0925) loss_box_reg: 0.1414 (0.1498) loss_objectness: 0.0034 (0.0043) loss_rpn_box_reg: 0.0077 (0.0087) time: 1.0397 data: 0.0205 max mem: 6508 Epoch: [9] [ 60/104] eta: 0:00:46 lr: 0.000001 loss: 0.2391 (0.2557) loss_classifier: 0.0853 (0.0925) loss_box_reg: 0.1478 (0.1502) loss_objectness: 0.0037 (0.0043) loss_rpn_box_reg: 0.0073 (0.0088) time: 1.0324 data: 0.0208 max mem: 6508 Epoch: [9] [ 70/104] eta: 0:00:36 lr: 0.000001 loss: 0.2392 (0.2576) loss_classifier: 0.0898 (0.0927) loss_box_reg: 0.1430 (0.1518) loss_objectness: 0.0033 (0.0042) loss_rpn_box_reg: 0.0072 (0.0089) time: 1.0274 data: 0.0208 max mem: 6508 Epoch: [9] [ 80/104] eta: 0:00:25 lr: 0.000001 loss: 0.2606 (0.2628) loss_classifier: 0.0934 (0.0937) loss_box_reg: 0.1607 (0.1561) loss_objectness: 0.0022 (0.0040) loss_rpn_box_reg: 0.0072 (0.0090) time: 1.0352 data: 0.0209 max mem: 6508 Epoch: [9] [ 90/104] eta: 0:00:14 lr: 0.000001 loss: 0.2535 (0.2627) loss_classifier: 0.0889 (0.0935) loss_box_reg: 0.1581 (0.1562) loss_objectness: 0.0030 (0.0041) loss_rpn_box_reg: 0.0065 (0.0090) time: 1.0406 data: 0.0204 max mem: 6508 Epoch: [9] [100/104] eta: 0:00:04 lr: 0.000001 loss: 0.2500 (0.2615) loss_classifier: 0.0813 (0.0928) loss_box_reg: 0.1495 (0.1559) loss_objectness: 0.0036 (0.0040) loss_rpn_box_reg: 0.0060 (0.0087) time: 1.0400 data: 0.0197 max mem: 6508 Epoch: [9] [103/104] eta: 0:00:01 lr: 0.000001 loss: 0.2444 (0.2629) loss_classifier: 0.0784 (0.0932) loss_box_reg: 0.1495 (0.1569) loss_objectness: 0.0037 (0.0041) loss_rpn_box_reg: 0.0062 (0.0087) time: 1.0406 data: 0.0196 max mem: 6508 Epoch: [9] Total time: 0:01:49 (1.0562 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.4913 (0.4913) evaluator_time: 0.0282 (0.0282) time: 1.2392 data: 0.6994 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4355 (0.4357) evaluator_time: 0.0149 (0.0173) time: 0.4806 data: 0.0211 max mem: 6508 Test: Total time: 0:00:13 (0.5108 s / it) Averaged stats: model_time: 0.4355 (0.4357) evaluator_time: 0.0149 (0.0173) Accumulating evaluation results... DONE (t=0.11s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.485 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.830 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.489 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.545 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.383 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.519 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.574 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.447 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.622 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.518 Epoch: [10] [ 0/104] eta: 0:03:18 lr: 0.000001 loss: 0.3089 (0.3089) loss_classifier: 0.1102 (0.1102) loss_box_reg: 0.1835 (0.1835) loss_objectness: 0.0038 (0.0038) loss_rpn_box_reg: 0.0114 (0.0114) time: 1.9111 data: 0.8486 max mem: 6508 Epoch: [10] [ 10/104] eta: 0:01:47 lr: 0.000001 loss: 0.2657 (0.2665) loss_classifier: 0.0903 (0.0977) loss_box_reg: 0.1476 (0.1562) loss_objectness: 0.0035 (0.0042) loss_rpn_box_reg: 0.0075 (0.0084) time: 1.1399 data: 0.0912 max mem: 6508 Epoch: [10] [ 20/104] eta: 0:01:35 lr: 0.000001 loss: 0.2605 (0.2809) loss_classifier: 0.0932 (0.1030) loss_box_reg: 0.1476 (0.1628) loss_objectness: 0.0034 (0.0059) loss_rpn_box_reg: 0.0076 (0.0092) time: 1.0934 data: 0.0224 max mem: 6508 Epoch: [10] [ 30/104] eta: 0:01:25 lr: 0.000001 loss: 0.2682 (0.2800) loss_classifier: 0.0955 (0.1011) loss_box_reg: 0.1592 (0.1640) loss_objectness: 0.0035 (0.0051) loss_rpn_box_reg: 0.0078 (0.0098) time: 1.1623 data: 0.0358 max mem: 6508 Epoch: [10] [ 40/104] eta: 0:01:12 lr: 0.000001 loss: 0.2551 (0.2719) loss_classifier: 0.0816 (0.0969) loss_box_reg: 0.1583 (0.1608) loss_objectness: 0.0033 (0.0047) loss_rpn_box_reg: 0.0069 (0.0094) time: 1.1410 data: 0.0376 max mem: 6508 Epoch: [10] [ 50/104] eta: 0:01:00 lr: 0.000001 loss: 0.2229 (0.2650) loss_classifier: 0.0771 (0.0944) loss_box_reg: 0.1324 (0.1573) loss_objectness: 0.0032 (0.0044) loss_rpn_box_reg: 0.0054 (0.0089) time: 1.0621 data: 0.0288 max mem: 6508 Epoch: [10] [ 60/104] eta: 0:00:48 lr: 0.000001 loss: 0.2212 (0.2611) loss_classifier: 0.0734 (0.0934) loss_box_reg: 0.1324 (0.1546) loss_objectness: 0.0029 (0.0043) loss_rpn_box_reg: 0.0052 (0.0088) time: 1.0553 data: 0.0267 max mem: 6508 Epoch: [10] [ 70/104] eta: 0:00:37 lr: 0.000001 loss: 0.2255 (0.2605) loss_classifier: 0.0734 (0.0928) loss_box_reg: 0.1316 (0.1547) loss_objectness: 0.0038 (0.0042) loss_rpn_box_reg: 0.0060 (0.0087) time: 1.0582 data: 0.0265 max mem: 6508 Epoch: [10] [ 80/104] eta: 0:00:26 lr: 0.000001 loss: 0.2585 (0.2621) loss_classifier: 0.0922 (0.0928) loss_box_reg: 0.1622 (0.1563) loss_objectness: 0.0034 (0.0041) loss_rpn_box_reg: 0.0080 (0.0089) time: 1.0439 data: 0.0230 max mem: 6508 Epoch: [10] [ 90/104] eta: 0:00:15 lr: 0.000001 loss: 0.2750 (0.2630) loss_classifier: 0.0931 (0.0927) loss_box_reg: 0.1622 (0.1573) loss_objectness: 0.0034 (0.0042) loss_rpn_box_reg: 0.0080 (0.0089) time: 1.0459 data: 0.0230 max mem: 6508 Epoch: [10] [100/104] eta: 0:00:04 lr: 0.000001 loss: 0.2316 (0.2620) loss_classifier: 0.0895 (0.0925) loss_box_reg: 0.1439 (0.1569) loss_objectness: 0.0026 (0.0040) loss_rpn_box_reg: 0.0062 (0.0087) time: 1.0466 data: 0.0214 max mem: 6508 Epoch: [10] [103/104] eta: 0:00:01 lr: 0.000001 loss: 0.2247 (0.2615) loss_classifier: 0.0815 (0.0924) loss_box_reg: 0.1439 (0.1564) loss_objectness: 0.0025 (0.0040) loss_rpn_box_reg: 0.0065 (0.0087) time: 1.0447 data: 0.0206 max mem: 6508 Epoch: [10] Total time: 0:01:52 (1.0842 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:33 model_time: 0.4919 (0.4919) evaluator_time: 0.0472 (0.0472) time: 1.2793 data: 0.7246 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4423 (0.4451) evaluator_time: 0.0137 (0.0207) time: 0.4948 data: 0.0227 max mem: 6508 Test: Total time: 0:00:13 (0.5284 s / it) Averaged stats: model_time: 0.4423 (0.4451) evaluator_time: 0.0137 (0.0207) Accumulating evaluation results... DONE (t=0.12s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.488 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.833 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.504 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.346 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.548 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.393 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.213 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.523 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.578 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.447 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.626 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.532 Epoch: [11] [ 0/104] eta: 0:03:19 lr: 0.000001 loss: 0.2695 (0.2695) loss_classifier: 0.0898 (0.0898) loss_box_reg: 0.1705 (0.1705) loss_objectness: 0.0016 (0.0016) loss_rpn_box_reg: 0.0075 (0.0075) time: 1.9135 data: 0.7632 max mem: 6508 Epoch: [11] [ 10/104] eta: 0:01:48 lr: 0.000001 loss: 0.2695 (0.2629) loss_classifier: 0.0898 (0.0921) loss_box_reg: 0.1633 (0.1565) loss_objectness: 0.0020 (0.0031) loss_rpn_box_reg: 0.0095 (0.0111) time: 1.1551 data: 0.0876 max mem: 6508 Epoch: [11] [ 20/104] eta: 0:01:33 lr: 0.000001 loss: 0.2545 (0.2668) loss_classifier: 0.0900 (0.0933) loss_box_reg: 0.1567 (0.1594) loss_objectness: 0.0022 (0.0033) loss_rpn_box_reg: 0.0080 (0.0107) time: 1.0751 data: 0.0213 max mem: 6508 Epoch: [11] [ 30/104] eta: 0:01:21 lr: 0.000001 loss: 0.2302 (0.2587) loss_classifier: 0.0824 (0.0928) loss_box_reg: 0.1386 (0.1530) loss_objectness: 0.0025 (0.0035) loss_rpn_box_reg: 0.0062 (0.0095) time: 1.0620 data: 0.0225 max mem: 6508 Epoch: [11] [ 40/104] eta: 0:01:09 lr: 0.000001 loss: 0.2295 (0.2561) loss_classifier: 0.0814 (0.0911) loss_box_reg: 0.1414 (0.1524) loss_objectness: 0.0024 (0.0033) loss_rpn_box_reg: 0.0055 (0.0093) time: 1.0423 data: 0.0211 max mem: 6508 Epoch: [11] [ 50/104] eta: 0:00:57 lr: 0.000001 loss: 0.2392 (0.2601) loss_classifier: 0.0881 (0.0922) loss_box_reg: 0.1480 (0.1548) loss_objectness: 0.0032 (0.0037) loss_rpn_box_reg: 0.0062 (0.0094) time: 1.0284 data: 0.0201 max mem: 6508 Epoch: [11] [ 60/104] eta: 0:00:46 lr: 0.000001 loss: 0.2893 (0.2606) loss_classifier: 0.0993 (0.0921) loss_box_reg: 0.1679 (0.1556) loss_objectness: 0.0031 (0.0037) loss_rpn_box_reg: 0.0080 (0.0092) time: 1.0265 data: 0.0197 max mem: 6508 Epoch: [11] [ 70/104] eta: 0:00:35 lr: 0.000001 loss: 0.2819 (0.2620) loss_classifier: 0.0993 (0.0922) loss_box_reg: 0.1679 (0.1571) loss_objectness: 0.0029 (0.0037) loss_rpn_box_reg: 0.0071 (0.0089) time: 1.0316 data: 0.0195 max mem: 6508 Epoch: [11] [ 80/104] eta: 0:00:25 lr: 0.000001 loss: 0.2583 (0.2619) loss_classifier: 0.0874 (0.0921) loss_box_reg: 0.1529 (0.1574) loss_objectness: 0.0028 (0.0037) loss_rpn_box_reg: 0.0071 (0.0087) time: 1.0343 data: 0.0199 max mem: 6508 Epoch: [11] [ 90/104] eta: 0:00:14 lr: 0.000001 loss: 0.2398 (0.2594) loss_classifier: 0.0854 (0.0916) loss_box_reg: 0.1406 (0.1555) loss_objectness: 0.0026 (0.0037) loss_rpn_box_reg: 0.0066 (0.0086) time: 1.0461 data: 0.0237 max mem: 6508 Epoch: [11] [100/104] eta: 0:00:04 lr: 0.000001 loss: 0.2494 (0.2586) loss_classifier: 0.0855 (0.0914) loss_box_reg: 0.1472 (0.1548) loss_objectness: 0.0029 (0.0037) loss_rpn_box_reg: 0.0070 (0.0086) time: 1.0521 data: 0.0247 max mem: 6508 Epoch: [11] [103/104] eta: 0:00:01 lr: 0.000001 loss: 0.2512 (0.2602) loss_classifier: 0.0885 (0.0920) loss_box_reg: 0.1508 (0.1557) loss_objectness: 0.0036 (0.0039) loss_rpn_box_reg: 0.0072 (0.0087) time: 1.0448 data: 0.0215 max mem: 6508 Epoch: [11] Total time: 0:01:49 (1.0555 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:33 model_time: 0.5373 (0.5373) evaluator_time: 0.0450 (0.0450) time: 1.2868 data: 0.6877 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4405 (0.4423) evaluator_time: 0.0152 (0.0187) time: 0.4872 data: 0.0222 max mem: 6508 Test: Total time: 0:00:13 (0.5198 s / it) Averaged stats: model_time: 0.4405 (0.4423) evaluator_time: 0.0152 (0.0187) Accumulating evaluation results... DONE (t=0.11s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.490 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.833 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.511 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.346 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.396 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.524 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.579 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.448 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.628 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.523 Epoch: [12] [ 0/104] eta: 0:03:19 lr: 0.000000 loss: 0.3497 (0.3497) loss_classifier: 0.1162 (0.1162) loss_box_reg: 0.2069 (0.2069) loss_objectness: 0.0060 (0.0060) loss_rpn_box_reg: 0.0205 (0.0205) time: 1.9170 data: 0.8726 max mem: 6508 Epoch: [12] [ 10/104] eta: 0:01:47 lr: 0.000000 loss: 0.2639 (0.2456) loss_classifier: 0.0837 (0.0876) loss_box_reg: 0.1679 (0.1477) loss_objectness: 0.0025 (0.0031) loss_rpn_box_reg: 0.0052 (0.0071) time: 1.1453 data: 0.0969 max mem: 6508 Epoch: [12] [ 20/104] eta: 0:01:33 lr: 0.000000 loss: 0.2498 (0.2473) loss_classifier: 0.0865 (0.0913) loss_box_reg: 0.1379 (0.1448) loss_objectness: 0.0034 (0.0034) loss_rpn_box_reg: 0.0062 (0.0078) time: 1.0765 data: 0.0202 max mem: 6508 Epoch: [12] [ 30/104] eta: 0:01:21 lr: 0.000000 loss: 0.2490 (0.2526) loss_classifier: 0.0865 (0.0924) loss_box_reg: 0.1386 (0.1474) loss_objectness: 0.0047 (0.0044) loss_rpn_box_reg: 0.0075 (0.0084) time: 1.0742 data: 0.0201 max mem: 6508 Epoch: [12] [ 40/104] eta: 0:01:09 lr: 0.000000 loss: 0.2490 (0.2493) loss_classifier: 0.0856 (0.0914) loss_box_reg: 0.1379 (0.1453) loss_objectness: 0.0039 (0.0043) loss_rpn_box_reg: 0.0075 (0.0083) time: 1.0522 data: 0.0191 max mem: 6508 Epoch: [12] [ 50/104] eta: 0:00:58 lr: 0.000000 loss: 0.2500 (0.2548) loss_classifier: 0.0856 (0.0921) loss_box_reg: 0.1310 (0.1499) loss_objectness: 0.0031 (0.0042) loss_rpn_box_reg: 0.0074 (0.0086) time: 1.0406 data: 0.0189 max mem: 6508 Epoch: [12] [ 60/104] eta: 0:00:46 lr: 0.000000 loss: 0.3066 (0.2637) loss_classifier: 0.0943 (0.0945) loss_box_reg: 0.1809 (0.1558) loss_objectness: 0.0040 (0.0043) loss_rpn_box_reg: 0.0091 (0.0090) time: 1.0306 data: 0.0201 max mem: 6508 Epoch: [12] [ 70/104] eta: 0:00:36 lr: 0.000000 loss: 0.2656 (0.2608) loss_classifier: 0.0877 (0.0930) loss_box_reg: 0.1641 (0.1547) loss_objectness: 0.0038 (0.0043) loss_rpn_box_reg: 0.0085 (0.0088) time: 1.0225 data: 0.0218 max mem: 6508 Epoch: [12] [ 80/104] eta: 0:00:25 lr: 0.000000 loss: 0.2258 (0.2579) loss_classifier: 0.0735 (0.0922) loss_box_reg: 0.1359 (0.1531) loss_objectness: 0.0026 (0.0041) loss_rpn_box_reg: 0.0066 (0.0086) time: 1.0465 data: 0.0231 max mem: 6508 Epoch: [12] [ 90/104] eta: 0:00:14 lr: 0.000000 loss: 0.2576 (0.2613) loss_classifier: 0.0903 (0.0929) loss_box_reg: 0.1535 (0.1554) loss_objectness: 0.0026 (0.0042) loss_rpn_box_reg: 0.0069 (0.0089) time: 1.0554 data: 0.0229 max mem: 6508 Epoch: [12] [100/104] eta: 0:00:04 lr: 0.000000 loss: 0.2593 (0.2589) loss_classifier: 0.0903 (0.0915) loss_box_reg: 0.1555 (0.1548) loss_objectness: 0.0030 (0.0041) loss_rpn_box_reg: 0.0068 (0.0086) time: 1.0412 data: 0.0205 max mem: 6508 Epoch: [12] [103/104] eta: 0:00:01 lr: 0.000000 loss: 0.2605 (0.2601) loss_classifier: 0.0918 (0.0920) loss_box_reg: 0.1555 (0.1553) loss_objectness: 0.0030 (0.0041) loss_rpn_box_reg: 0.0065 (0.0087) time: 1.0399 data: 0.0198 max mem: 6508 Epoch: [12] Total time: 0:01:50 (1.0596 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:40 model_time: 0.4978 (0.4978) evaluator_time: 0.0399 (0.0399) time: 1.5401 data: 0.9932 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4383 (0.4400) evaluator_time: 0.0135 (0.0174) time: 0.4859 data: 0.0217 max mem: 6508 Test: Total time: 0:00:13 (0.5286 s / it) Averaged stats: model_time: 0.4383 (0.4400) evaluator_time: 0.0135 (0.0174) Accumulating evaluation results... DONE (t=0.11s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.491 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.833 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.514 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.347 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.396 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.216 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.524 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.580 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.448 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.629 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.523 Epoch: [13] [ 0/104] eta: 0:03:24 lr: 0.000000 loss: 0.2393 (0.2393) loss_classifier: 0.0915 (0.0915) loss_box_reg: 0.1408 (0.1408) loss_objectness: 0.0010 (0.0010) loss_rpn_box_reg: 0.0061 (0.0061) time: 1.9701 data: 0.8935 max mem: 6508 Epoch: [13] [ 10/104] eta: 0:01:48 lr: 0.000000 loss: 0.2910 (0.2708) loss_classifier: 0.1009 (0.0982) loss_box_reg: 0.1593 (0.1614) loss_objectness: 0.0021 (0.0034) loss_rpn_box_reg: 0.0082 (0.0078) time: 1.1562 data: 0.1002 max mem: 6508 Epoch: [13] [ 20/104] eta: 0:01:34 lr: 0.000000 loss: 0.2833 (0.2792) loss_classifier: 0.1029 (0.1013) loss_box_reg: 0.1593 (0.1650) loss_objectness: 0.0035 (0.0041) loss_rpn_box_reg: 0.0085 (0.0089) time: 1.0812 data: 0.0229 max mem: 6508 Epoch: [13] [ 30/104] eta: 0:01:21 lr: 0.000000 loss: 0.2602 (0.2660) loss_classifier: 0.0801 (0.0950) loss_box_reg: 0.1569 (0.1582) loss_objectness: 0.0033 (0.0040) loss_rpn_box_reg: 0.0079 (0.0089) time: 1.0702 data: 0.0222 max mem: 6508 Epoch: [13] [ 40/104] eta: 0:01:09 lr: 0.000000 loss: 0.2506 (0.2652) loss_classifier: 0.0801 (0.0937) loss_box_reg: 0.1573 (0.1587) loss_objectness: 0.0028 (0.0041) loss_rpn_box_reg: 0.0074 (0.0087) time: 1.0433 data: 0.0199 max mem: 6508 Epoch: [13] [ 50/104] eta: 0:00:57 lr: 0.000000 loss: 0.2642 (0.2710) loss_classifier: 0.0905 (0.0958) loss_box_reg: 0.1610 (0.1622) loss_objectness: 0.0033 (0.0039) loss_rpn_box_reg: 0.0074 (0.0091) time: 1.0326 data: 0.0208 max mem: 6508 Epoch: [13] [ 60/104] eta: 0:00:46 lr: 0.000000 loss: 0.2453 (0.2643) loss_classifier: 0.0866 (0.0942) loss_box_reg: 0.1528 (0.1572) loss_objectness: 0.0037 (0.0041) loss_rpn_box_reg: 0.0067 (0.0089) time: 1.0309 data: 0.0213 max mem: 6508 Epoch: [13] [ 70/104] eta: 0:00:36 lr: 0.000000 loss: 0.2453 (0.2652) loss_classifier: 0.0866 (0.0948) loss_box_reg: 0.1528 (0.1573) loss_objectness: 0.0042 (0.0041) loss_rpn_box_reg: 0.0079 (0.0090) time: 1.0320 data: 0.0206 max mem: 6508 Epoch: [13] [ 80/104] eta: 0:00:25 lr: 0.000000 loss: 0.2683 (0.2626) loss_classifier: 0.0866 (0.0937) loss_box_reg: 0.1559 (0.1561) loss_objectness: 0.0035 (0.0041) loss_rpn_box_reg: 0.0085 (0.0087) time: 1.0314 data: 0.0197 max mem: 6508 Epoch: [13] [ 90/104] eta: 0:00:14 lr: 0.000000 loss: 0.2463 (0.2607) loss_classifier: 0.0811 (0.0925) loss_box_reg: 0.1417 (0.1554) loss_objectness: 0.0035 (0.0041) loss_rpn_box_reg: 0.0064 (0.0087) time: 1.0334 data: 0.0196 max mem: 6508 Epoch: [13] [100/104] eta: 0:00:04 lr: 0.000000 loss: 0.2353 (0.2590) loss_classifier: 0.0811 (0.0918) loss_box_reg: 0.1483 (0.1546) loss_objectness: 0.0020 (0.0039) loss_rpn_box_reg: 0.0069 (0.0086) time: 1.0410 data: 0.0193 max mem: 6508 Epoch: [13] [103/104] eta: 0:00:01 lr: 0.000000 loss: 0.2353 (0.2591) loss_classifier: 0.0850 (0.0920) loss_box_reg: 0.1483 (0.1545) loss_objectness: 0.0021 (0.0039) loss_rpn_box_reg: 0.0070 (0.0086) time: 1.0392 data: 0.0185 max mem: 6508 Epoch: [13] Total time: 0:01:49 (1.0554 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:34 model_time: 0.5242 (0.5242) evaluator_time: 0.0259 (0.0259) time: 1.3083 data: 0.7425 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4377 (0.4397) evaluator_time: 0.0137 (0.0181) time: 0.4854 data: 0.0227 max mem: 6508 Test: Total time: 0:00:13 (0.5196 s / it) Averaged stats: model_time: 0.4377 (0.4397) evaluator_time: 0.0137 (0.0181) Accumulating evaluation results... DONE (t=0.13s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.491 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.833 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.514 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.347 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.396 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.216 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.524 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.580 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.448 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.629 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.523 Epoch: [14] [ 0/104] eta: 0:03:12 lr: 0.000000 loss: 0.3410 (0.3410) loss_classifier: 0.1312 (0.1312) loss_box_reg: 0.1974 (0.1974) loss_objectness: 0.0029 (0.0029) loss_rpn_box_reg: 0.0096 (0.0096) time: 1.8523 data: 0.7088 max mem: 6508 Epoch: [14] [ 10/104] eta: 0:01:46 lr: 0.000000 loss: 0.2914 (0.2693) loss_classifier: 0.1028 (0.0986) loss_box_reg: 0.1671 (0.1594) loss_objectness: 0.0027 (0.0031) loss_rpn_box_reg: 0.0082 (0.0081) time: 1.1355 data: 0.0833 max mem: 6508 Epoch: [14] [ 20/104] eta: 0:01:33 lr: 0.000000 loss: 0.2498 (0.2603) loss_classifier: 0.0872 (0.0934) loss_box_reg: 0.1375 (0.1540) loss_objectness: 0.0027 (0.0036) loss_rpn_box_reg: 0.0078 (0.0092) time: 1.0747 data: 0.0225 max mem: 6508 Epoch: [14] [ 30/104] eta: 0:01:20 lr: 0.000000 loss: 0.2601 (0.2608) loss_classifier: 0.0880 (0.0931) loss_box_reg: 0.1574 (0.1557) loss_objectness: 0.0030 (0.0036) loss_rpn_box_reg: 0.0063 (0.0085) time: 1.0702 data: 0.0222 max mem: 6508 Epoch: [14] [ 40/104] eta: 0:01:09 lr: 0.000000 loss: 0.2729 (0.2664) loss_classifier: 0.0955 (0.0959) loss_box_reg: 0.1624 (0.1577) loss_objectness: 0.0032 (0.0043) loss_rpn_box_reg: 0.0082 (0.0085) time: 1.0459 data: 0.0205 max mem: 6508 Epoch: [14] [ 50/104] eta: 0:00:57 lr: 0.000000 loss: 0.2713 (0.2574) loss_classifier: 0.0907 (0.0935) loss_box_reg: 0.1412 (0.1514) loss_objectness: 0.0025 (0.0041) loss_rpn_box_reg: 0.0082 (0.0083) time: 1.0353 data: 0.0213 max mem: 6508 Epoch: [14] [ 60/104] eta: 0:00:46 lr: 0.000000 loss: 0.2197 (0.2578) loss_classifier: 0.0854 (0.0934) loss_box_reg: 0.1366 (0.1518) loss_objectness: 0.0021 (0.0039) loss_rpn_box_reg: 0.0065 (0.0087) time: 1.0321 data: 0.0218 max mem: 6508 Epoch: [14] [ 70/104] eta: 0:00:36 lr: 0.000000 loss: 0.2562 (0.2615) loss_classifier: 0.0945 (0.0944) loss_box_reg: 0.1455 (0.1542) loss_objectness: 0.0028 (0.0041) loss_rpn_box_reg: 0.0077 (0.0088) time: 1.0295 data: 0.0213 max mem: 6508 Epoch: [14] [ 80/104] eta: 0:00:25 lr: 0.000000 loss: 0.2723 (0.2631) loss_classifier: 0.0945 (0.0939) loss_box_reg: 0.1650 (0.1559) loss_objectness: 0.0033 (0.0041) loss_rpn_box_reg: 0.0102 (0.0092) time: 1.0303 data: 0.0202 max mem: 6508 Epoch: [14] [ 90/104] eta: 0:00:14 lr: 0.000000 loss: 0.2576 (0.2605) loss_classifier: 0.0889 (0.0929) loss_box_reg: 0.1560 (0.1547) loss_objectness: 0.0022 (0.0040) loss_rpn_box_reg: 0.0074 (0.0089) time: 1.0369 data: 0.0208 max mem: 6508 Epoch: [14] [100/104] eta: 0:00:04 lr: 0.000000 loss: 0.2431 (0.2599) loss_classifier: 0.0874 (0.0921) loss_box_reg: 0.1418 (0.1552) loss_objectness: 0.0022 (0.0039) loss_rpn_box_reg: 0.0055 (0.0087) time: 1.0391 data: 0.0203 max mem: 6508 Epoch: [14] [103/104] eta: 0:00:01 lr: 0.000000 loss: 0.2379 (0.2594) loss_classifier: 0.0836 (0.0920) loss_box_reg: 0.1418 (0.1549) loss_objectness: 0.0022 (0.0039) loss_rpn_box_reg: 0.0055 (0.0086) time: 1.0370 data: 0.0194 max mem: 6508 Epoch: [14] Total time: 0:01:49 (1.0532 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:31 model_time: 0.5288 (0.5288) evaluator_time: 0.0853 (0.0853) time: 1.2276 data: 0.5798 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4429 (0.4433) evaluator_time: 0.0207 (0.0252) time: 0.4958 data: 0.0231 max mem: 6508 Test: Total time: 0:00:13 (0.5262 s / it) Averaged stats: model_time: 0.4429 (0.4433) evaluator_time: 0.0207 (0.0252) Accumulating evaluation results... DONE (t=0.11s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.491 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.833 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.514 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.347 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.396 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.216 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.524 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.580 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.448 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.629 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.523
#save adamn
import pickle
Filename = "FRCNN2adamn.pkl"
# Define the file path where you want to save the model
filename = "/content/drive/MyDrive/dataset1/FRCNN2adamn.pkl"
# Save the model to the specified file path
torch.save(model.state_dict(), filename)
# Save the Modle to file in the current working directory
with open(Filename, 'wb') as file:
pickle.dump(model, file)
# Load the Model back from file
with open(Filename, 'rb') as file:
model = pickle.load(file)
model
FasterRCNN(
(transform): GeneralizedRCNNTransform(
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
Resize(min_size=(800,), max_size=1333, mode='bilinear')
)
(backbone): BackboneWithFPN(
(body): IntermediateLayerGetter(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): FrozenBatchNorm2d(256, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(512, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(1024, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(2048, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
)
)
(fpn): FeaturePyramidNetwork(
(inner_blocks): ModuleList(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
)
(1): Conv2dNormActivation(
(0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
(2): Conv2dNormActivation(
(0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
)
(3): Conv2dNormActivation(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
)
)
(layer_blocks): ModuleList(
(0-3): 4 x Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(extra_blocks): LastLevelMaxPool()
)
)
(rpn): RegionProposalNetwork(
(anchor_generator): AnchorGenerator()
(head): RPNHead(
(conv): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
)
)
(cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
(bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
)
)
(roi_heads): RoIHeads(
(box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)
(box_head): TwoMLPHead(
(fc6): Linear(in_features=12544, out_features=1024, bias=True)
(fc7): Linear(in_features=1024, out_features=1024, bias=True)
)
(box_predictor): FastRCNNPredictor(
(cls_score): Linear(in_features=1024, out_features=11, bias=True)
(bbox_pred): Linear(in_features=1024, out_features=44, bias=True)
)
)
)
#rms prob
# to train on GPU if selected
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# number of classes
num_classes = 11
# get the model using our helper function
model = get_object_detection_model(num_classes)
# move model to the right device
model.to(device)
# construct an optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.RMSprop(params, lr=0.001, weight_decay=0.0005)
# and a learning rate scheduler which decreases the learning rate by
# 10x every 3 epochs
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=3,
gamma=0.1)
# training for 8 epochs # rmsprob
num_epochs = 15
for epoch in range(num_epochs):
# training for one epoch
train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
# update the learning rate
lr_scheduler.step()
# evaluate on the test dataset
evaluate(model, data_loader_test, device=device)
Epoch: [0] [ 0/104] eta: 0:04:00 lr: 0.000011 loss: 3.2039 (3.2039) loss_classifier: 2.6346 (2.6346) loss_box_reg: 0.2353 (0.2353) loss_objectness: 0.3166 (0.3166) loss_rpn_box_reg: 0.0173 (0.0173) time: 2.3164 data: 0.8751 max mem: 6508 Epoch: [0] [ 10/104] eta: 0:01:58 lr: 0.000108 loss: 1.2988 (2.1030) loss_classifier: 0.7136 (1.1584) loss_box_reg: 0.2786 (0.2337) loss_objectness: 0.3166 (0.6631) loss_rpn_box_reg: 0.0292 (0.0478) time: 1.2616 data: 0.1041 max mem: 6508 Epoch: [0] [ 20/104] eta: 0:01:44 lr: 0.000205 loss: 1.2517 (1.9053) loss_classifier: 0.5494 (1.1040) loss_box_reg: 0.2690 (0.2812) loss_objectness: 0.2239 (0.4791) loss_rpn_box_reg: 0.0292 (0.0410) time: 1.1858 data: 0.0383 max mem: 6508 Epoch: [0] [ 30/104] eta: 0:01:27 lr: 0.000302 loss: 1.0549 (1.6985) loss_classifier: 0.6310 (0.9926) loss_box_reg: 0.2180 (0.2768) loss_objectness: 0.1345 (0.3931) loss_rpn_box_reg: 0.0252 (0.0360) time: 1.1374 data: 0.0344 max mem: 6508 Epoch: [0] [ 40/104] eta: 0:01:13 lr: 0.000399 loss: 1.3226 (3.2637) loss_classifier: 0.7431 (1.0957) loss_box_reg: 0.2201 (0.4396) loss_objectness: 0.2049 (1.5802) loss_rpn_box_reg: 0.0295 (0.1482) time: 1.0445 data: 0.0196 max mem: 6508 Epoch: [0] [ 50/104] eta: 0:01:00 lr: 0.000496 loss: 2.6148 (2047.7227) loss_classifier: 1.1095 (320.6169) loss_box_reg: 0.2412 (1473.0099) loss_objectness: 0.6097 (235.9155) loss_rpn_box_reg: 0.0607 (18.1803) time: 1.0051 data: 0.0208 max mem: 6508 Epoch: [0] [ 60/104] eta: 0:00:48 lr: 0.000593 loss: 2.2350 (1712.3068) loss_classifier: 0.6001 (268.1364) loss_box_reg: 0.1415 (1231.5624) loss_objectness: 0.6692 (197.3820) loss_rpn_box_reg: 0.1377 (15.2260) time: 0.9892 data: 0.0232 max mem: 6508 Epoch: [0] [ 70/104] eta: 0:00:36 lr: 0.000690 loss: 0.9199 (1471.2591) loss_classifier: 0.3719 (230.4266) loss_box_reg: 0.1484 (1058.1248) loss_objectness: 0.3157 (169.6188) loss_rpn_box_reg: 0.0653 (13.0889) time: 1.0034 data: 0.0225 max mem: 6508 Epoch: [0] [ 80/104] eta: 0:00:25 lr: 0.000787 loss: 0.6987 (1289.6876) loss_classifier: 0.2646 (202.0060) loss_box_reg: 0.1104 (927.5039) loss_objectness: 0.1908 (148.7011) loss_rpn_box_reg: 0.0342 (11.4766) time: 1.0148 data: 0.0234 max mem: 6508 Epoch: [0] [ 90/104] eta: 0:00:14 lr: 0.000884 loss: 0.5503 (1148.0251) loss_classifier: 0.2011 (179.8337) loss_box_reg: 0.1104 (825.5942) loss_objectness: 0.1656 (132.3790) loss_rpn_box_reg: 0.0246 (10.2181) time: 1.0232 data: 0.0248 max mem: 6508 Epoch: [0] [100/104] eta: 0:00:04 lr: 0.000981 loss: 0.6267 (1034.4380) loss_classifier: 0.2911 (162.0643) loss_box_reg: 0.1404 (743.8696) loss_objectness: 0.2006 (119.2944) loss_rpn_box_reg: 0.0273 (9.2096) time: 1.0243 data: 0.0203 max mem: 6508 Epoch: [0] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.6827 (1004.6290) loss_classifier: 0.3058 (157.3996) loss_box_reg: 0.1531 (722.4157) loss_objectness: 0.2081 (115.8637) loss_rpn_box_reg: 0.0323 (8.9500) time: 1.0285 data: 0.0204 max mem: 6508 Epoch: [0] Total time: 0:01:50 (1.0649 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:33 model_time: 0.4742 (0.4742) evaluator_time: 0.0052 (0.0052) time: 1.2894 data: 0.7832 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4315 (0.4316) evaluator_time: 0.0025 (0.0029) time: 0.4587 data: 0.0188 max mem: 6508 Test: Total time: 0:00:12 (0.4944 s / it) Averaged stats: model_time: 0.4315 (0.4316) evaluator_time: 0.0025 (0.0029) Accumulating evaluation results... DONE (t=0.07s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [1] [ 0/104] eta: 0:03:33 lr: 0.001000 loss: 1.4520 (1.4520) loss_classifier: 0.6604 (0.6604) loss_box_reg: 0.4759 (0.4759) loss_objectness: 0.2343 (0.2343) loss_rpn_box_reg: 0.0815 (0.0815) time: 2.0520 data: 1.0032 max mem: 6508 Epoch: [1] [ 10/104] eta: 0:01:37 lr: 0.001000 loss: 258.9862 (150766802.6828) loss_classifier: 127.6931 (34160226.4197) loss_box_reg: 100.0327 (111223218.1034) loss_objectness: 24.7305 (4924468.5647) loss_rpn_box_reg: 8.8204 (458898.8178) time: 1.0388 data: 0.1060 max mem: 6508 Epoch: [1] [ 20/104] eta: 0:01:21 lr: 0.001000 loss: 5199.8223 (110306276.1123) loss_classifier: 2014.8184 (25663462.8065) loss_box_reg: 2918.8621 (80794828.5285) loss_objectness: 210.1121 (3472368.6373) loss_rpn_box_reg: 56.0295 (375620.2806) time: 0.9218 data: 0.0179 max mem: 6508 Epoch: [1] [ 30/104] eta: 0:01:10 lr: 0.001000 loss: 2652.1675 (74724978.8528) loss_classifier: 1474.1609 (17385822.3312) loss_box_reg: 1091.9497 (54732404.6968) loss_objectness: 139.8561 (2352287.9434) loss_rpn_box_reg: 30.5260 (254466.6862) time: 0.9072 data: 0.0208 max mem: 6508 Epoch: [1] [ 40/104] eta: 0:01:00 lr: 0.001000 loss: 1029.4604 (56626685.1848) loss_classifier: 611.0850 (13175208.6310) loss_box_reg: 374.0753 (41479597.3672) loss_objectness: 93.0817 (1779160.0837) loss_rpn_box_reg: 24.7837 (192721.2135) time: 0.8983 data: 0.0207 max mem: 6508 Epoch: [1] [ 50/104] eta: 0:00:50 lr: 0.001000 loss: 660.6842 (46107377.0981) loss_classifier: 430.4982 (10690059.8973) loss_box_reg: 89.7603 (33828148.1769) loss_objectness: 88.0877 (1432500.1503) loss_rpn_box_reg: 20.9473 (156670.5400) time: 0.9033 data: 0.0195 max mem: 6508 Epoch: [1] [ 60/104] eta: 0:00:40 lr: 0.001000 loss: 426.3805 (38549403.6476) loss_classifier: 143.6905 (8937839.6519) loss_box_reg: 44.3117 (28282871.8341) loss_objectness: 109.9760 (1197698.4676) loss_rpn_box_reg: 24.6669 (130995.0873) time: 0.9105 data: 0.0201 max mem: 6508 Epoch: [1] [ 70/104] eta: 0:00:31 lr: 0.001000 loss: 1013.4094 (33122822.6003) loss_classifier: 778.9881 (7679878.7422) loss_box_reg: 142.9680 (24301273.3163) loss_objectness: 96.8082 (1029107.5885) loss_rpn_box_reg: 24.6669 (112564.1501) time: 0.8864 data: 0.0201 max mem: 6508 Epoch: [1] [ 80/104] eta: 0:00:22 lr: 0.001000 loss: 840.2881 (29035017.0729) loss_classifier: 403.8030 (6732312.8815) loss_box_reg: 67.4619 (21301945.7503) loss_objectness: 109.2580 (902086.1737) loss_rpn_box_reg: 32.2513 (98673.3166) time: 0.8857 data: 0.0209 max mem: 6508 Epoch: [1] [ 90/104] eta: 0:00:12 lr: 0.001000 loss: 199.0619 (25849412.5666) loss_classifier: 57.0748 (5994171.1934) loss_box_reg: 25.5540 (18964375.8005) loss_objectness: 109.2580 (803029.8781) loss_rpn_box_reg: 20.1335 (87836.6283) time: 0.9096 data: 0.0213 max mem: 6508 Epoch: [1] [100/104] eta: 0:00:03 lr: 0.001000 loss: 2028.7905 (23291665.0164) loss_classifier: 410.3585 (5401432.9579) loss_box_reg: 74.8636 (17087452.5200) loss_objectness: 100.7363 (723622.5691) loss_rpn_box_reg: 39.7727 (79157.8108) time: 0.9073 data: 0.0197 max mem: 6508 Epoch: [1] [103/104] eta: 0:00:00 lr: 0.001000 loss: 2464.8901 (22620711.5484) loss_classifier: 1564.9734 (5245974.8526) loss_box_reg: 123.4845 (16595103.2782) loss_objectness: 123.8959 (702756.3317) loss_rpn_box_reg: 52.1484 (76877.9029) time: 0.9007 data: 0.0198 max mem: 6508 Epoch: [1] Total time: 0:01:35 (0.9172 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:46 model_time: 0.5445 (0.5445) evaluator_time: 0.0575 (0.0575) time: 1.7704 data: 1.1407 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3761 (0.3835) evaluator_time: 0.0077 (0.0256) time: 0.4286 data: 0.0189 max mem: 6508 Test: Total time: 0:00:12 (0.4859 s / it) Averaged stats: model_time: 0.3761 (0.3835) evaluator_time: 0.0077 (0.0256) Accumulating evaluation results... DONE (t=0.21s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [2] [ 0/104] eta: 0:04:04 lr: 0.001000 loss: 3665.4360 (3665.4360) loss_classifier: 3275.9192 (3275.9192) loss_box_reg: 63.6991 (63.6991) loss_objectness: 234.6715 (234.6715) loss_rpn_box_reg: 91.1466 (91.1466) time: 2.3475 data: 1.2587 max mem: 6508 Epoch: [2] [ 10/104] eta: 0:01:38 lr: 0.001000 loss: 1106.7623 (1422.6658) loss_classifier: 562.6231 (1053.5947) loss_box_reg: 90.5225 (282.9638) loss_objectness: 47.2851 (66.0814) loss_rpn_box_reg: 10.7598 (20.0259) time: 1.0521 data: 0.1272 max mem: 6508 Epoch: [2] [ 20/104] eta: 0:01:23 lr: 0.001000 loss: 472.3109 (1020.1505) loss_classifier: 244.4862 (661.8328) loss_box_reg: 85.9072 (273.9741) loss_objectness: 43.4546 (62.3635) loss_rpn_box_reg: 14.1286 (21.9800) time: 0.9309 data: 0.0193 max mem: 6508 Epoch: [2] [ 30/104] eta: 0:01:12 lr: 0.001000 loss: 121.1059 (731.7680) loss_classifier: 41.7808 (463.9359) loss_box_reg: 22.7789 (198.2694) loss_objectness: 30.6498 (50.8209) loss_rpn_box_reg: 12.7459 (18.7418) time: 0.9380 data: 0.0235 max mem: 6508 Epoch: [2] [ 40/104] eta: 0:01:01 lr: 0.001000 loss: 133.5830 (634.6437) loss_classifier: 69.0693 (380.3669) loss_box_reg: 33.9993 (188.5627) loss_objectness: 25.5532 (47.8302) loss_rpn_box_reg: 10.1752 (17.8839) time: 0.9087 data: 0.0206 max mem: 6508 Epoch: [2] [ 50/104] eta: 0:00:50 lr: 0.001000 loss: 273.1735 (615.1750) loss_classifier: 69.0693 (345.7360) loss_box_reg: 79.6419 (199.5584) loss_objectness: 35.9543 (50.6717) loss_rpn_box_reg: 12.6669 (19.2088) time: 0.8914 data: 0.0195 max mem: 6508 Epoch: [2] [ 60/104] eta: 0:00:41 lr: 0.001000 loss: 365.2442 (3485.2267) loss_classifier: 124.5114 (1472.7287) loss_box_reg: 80.6085 (1905.5887) loss_objectness: 61.5172 (79.8159) loss_rpn_box_reg: 25.2533 (27.0934) time: 0.8913 data: 0.0218 max mem: 6508 Epoch: [2] [ 70/104] eta: 0:00:31 lr: 0.001000 loss: 594.5722 (3400.5566) loss_classifier: 283.9956 (1487.2163) loss_box_reg: 191.6195 (1795.6577) loss_objectness: 125.6146 (88.4065) loss_rpn_box_reg: 37.0607 (29.2761) time: 0.8710 data: 0.0222 max mem: 6508 Epoch: [2] [ 80/104] eta: 0:00:22 lr: 0.001000 loss: 490.2880 (3256.1005) loss_classifier: 126.4839 (1455.8884) loss_box_reg: 82.9989 (1676.2213) loss_objectness: 125.6146 (93.2964) loss_rpn_box_reg: 35.4058 (30.6944) time: 0.8967 data: 0.0251 max mem: 6508 Epoch: [2] [ 90/104] eta: 0:00:12 lr: 0.001000 loss: 517.9962 (3362.4710) loss_classifier: 175.8158 (1395.0213) loss_box_reg: 82.9989 (1828.0077) loss_objectness: 131.6742 (106.7316) loss_rpn_box_reg: 31.9363 (32.7103) time: 0.9032 data: 0.0249 max mem: 6508 Epoch: [2] [100/104] eta: 0:00:03 lr: 0.001000 loss: 480.0167 (3061.3139) loss_classifier: 99.8471 (1265.5824) loss_box_reg: 123.9330 (1659.4183) loss_objectness: 77.2580 (104.0533) loss_rpn_box_reg: 23.2427 (32.2599) time: 0.8693 data: 0.0208 max mem: 6508 Epoch: [2] [103/104] eta: 0:00:00 lr: 0.001000 loss: 315.5242 (2983.3835) loss_classifier: 89.9003 (1231.4645) loss_box_reg: 120.3518 (1615.6943) loss_objectness: 61.8435 (104.0673) loss_rpn_box_reg: 14.4586 (32.1574) time: 0.8691 data: 0.0202 max mem: 6508 Epoch: [2] Total time: 0:01:35 (0.9136 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:31 model_time: 0.4697 (0.4697) evaluator_time: 0.0529 (0.0529) time: 1.2306 data: 0.6546 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3741 (0.3791) evaluator_time: 0.0056 (0.0077) time: 0.4157 data: 0.0241 max mem: 6508 Test: Total time: 0:00:11 (0.4538 s / it) Averaged stats: model_time: 0.3741 (0.3791) evaluator_time: 0.0056 (0.0077) Accumulating evaluation results... DONE (t=0.09s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [3] [ 0/104] eta: 0:03:05 lr: 0.000100 loss: 304.2431 (304.2431) loss_classifier: 84.9447 (84.9447) loss_box_reg: 82.5549 (82.5549) loss_objectness: 105.4210 (105.4210) loss_rpn_box_reg: 31.3225 (31.3225) time: 1.7828 data: 0.9015 max mem: 6508 Epoch: [3] [ 10/104] eta: 0:01:30 lr: 0.000100 loss: 272.1476 (248.5491) loss_classifier: 39.4093 (47.7809) loss_box_reg: 107.1224 (101.2873) loss_objectness: 75.0196 (71.9039) loss_rpn_box_reg: 12.8808 (27.5771) time: 0.9625 data: 0.0974 max mem: 6508 Epoch: [3] [ 20/104] eta: 0:01:17 lr: 0.000100 loss: 265.4117 (264.0045) loss_classifier: 43.5565 (56.6697) loss_box_reg: 88.7669 (94.0715) loss_objectness: 75.0196 (82.9571) loss_rpn_box_reg: 13.7246 (30.3063) time: 0.8823 data: 0.0180 max mem: 6508 Epoch: [3] [ 30/104] eta: 0:01:07 lr: 0.000100 loss: 169.6590 (232.1553) loss_classifier: 35.6502 (46.1811) loss_box_reg: 55.9581 (74.7482) loss_objectness: 66.9722 (80.4461) loss_rpn_box_reg: 25.9141 (30.7800) time: 0.8803 data: 0.0193 max mem: 6508 Epoch: [3] [ 40/104] eta: 0:00:57 lr: 0.000100 loss: 148.1319 (219.2072) loss_classifier: 14.4577 (40.4276) loss_box_reg: 39.1762 (69.0179) loss_objectness: 67.2271 (79.7802) loss_rpn_box_reg: 28.7024 (29.9815) time: 0.8741 data: 0.0213 max mem: 6508 Epoch: [3] [ 50/104] eta: 0:00:48 lr: 0.000100 loss: 142.2928 (205.5259) loss_classifier: 12.6049 (34.9610) loss_box_reg: 47.2105 (64.0626) loss_objectness: 55.7569 (76.3784) loss_rpn_box_reg: 22.1210 (30.1239) time: 0.8627 data: 0.0208 max mem: 6508 Epoch: [3] [ 60/104] eta: 0:00:38 lr: 0.000100 loss: 133.3797 (195.1930) loss_classifier: 10.6868 (31.3746) loss_box_reg: 38.1073 (60.6654) loss_objectness: 46.0740 (73.7778) loss_rpn_box_reg: 17.6979 (29.3751) time: 0.8546 data: 0.0194 max mem: 6508 Epoch: [3] [ 70/104] eta: 0:00:29 lr: 0.000100 loss: 141.7962 (192.2237) loss_classifier: 8.9238 (28.4367) loss_box_reg: 38.1073 (59.2055) loss_objectness: 41.5079 (74.2889) loss_rpn_box_reg: 19.9482 (30.2926) time: 0.8553 data: 0.0202 max mem: 6508 Epoch: [3] [ 80/104] eta: 0:00:21 lr: 0.000100 loss: 97.7690 (183.0945) loss_classifier: 9.0629 (26.6522) loss_box_reg: 35.0960 (56.4913) loss_objectness: 40.6501 (70.6300) loss_rpn_box_reg: 13.6175 (29.3210) time: 0.8527 data: 0.0197 max mem: 6508 Epoch: [3] [ 90/104] eta: 0:00:12 lr: 0.000100 loss: 89.6644 (177.9917) loss_classifier: 10.7990 (25.1199) loss_box_reg: 29.3037 (56.3916) loss_objectness: 32.6587 (68.0665) loss_rpn_box_reg: 12.3882 (28.4137) time: 0.8554 data: 0.0211 max mem: 6508 Epoch: [3] [100/104] eta: 0:00:03 lr: 0.000100 loss: 91.6710 (171.5448) loss_classifier: 10.7990 (23.8506) loss_box_reg: 29.0876 (54.6277) loss_objectness: 41.3260 (65.3209) loss_rpn_box_reg: 13.0908 (27.7456) time: 0.8577 data: 0.0206 max mem: 6508 Epoch: [3] [103/104] eta: 0:00:00 lr: 0.000100 loss: 91.6710 (169.4891) loss_classifier: 10.7990 (23.4692) loss_box_reg: 25.1067 (54.1283) loss_objectness: 35.2736 (64.5671) loss_rpn_box_reg: 13.0908 (27.3245) time: 0.8590 data: 0.0207 max mem: 6508 Epoch: [3] Total time: 0:01:30 (0.8744 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:37 model_time: 0.4409 (0.4409) evaluator_time: 0.0071 (0.0071) time: 1.4601 data: 0.9932 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3669 (0.3692) evaluator_time: 0.0024 (0.0029) time: 0.3942 data: 0.0186 max mem: 6508 Test: Total time: 0:00:11 (0.4410 s / it) Averaged stats: model_time: 0.3669 (0.3692) evaluator_time: 0.0024 (0.0029) Accumulating evaluation results... DONE (t=0.07s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [4] [ 0/104] eta: 0:04:17 lr: 0.000100 loss: 66.1785 (66.1785) loss_classifier: 2.6410 (2.6410) loss_box_reg: 28.5581 (28.5581) loss_objectness: 26.6346 (26.6346) loss_rpn_box_reg: 8.3447 (8.3447) time: 2.4786 data: 1.2914 max mem: 6508 Epoch: [4] [ 10/104] eta: 0:01:36 lr: 0.000100 loss: 110.0946 (153.1176) loss_classifier: 9.2976 (12.9349) loss_box_reg: 35.5208 (49.3572) loss_objectness: 35.9742 (61.9151) loss_rpn_box_reg: 16.9071 (28.9103) time: 1.0231 data: 0.1354 max mem: 6508 Epoch: [4] [ 20/104] eta: 0:01:20 lr: 0.000100 loss: 110.0946 (135.4778) loss_classifier: 9.2976 (12.4413) loss_box_reg: 35.5208 (45.8977) loss_objectness: 36.0392 (52.7461) loss_rpn_box_reg: 13.6858 (24.3927) time: 0.8864 data: 0.0213 max mem: 6508 Epoch: [4] [ 30/104] eta: 0:01:09 lr: 0.000100 loss: 109.8829 (125.2450) loss_classifier: 11.8280 (14.1573) loss_box_reg: 24.4844 (41.4060) loss_objectness: 40.7744 (47.4909) loss_rpn_box_reg: 10.3591 (22.1907) time: 0.8879 data: 0.0227 max mem: 6508 Epoch: [4] [ 40/104] eta: 0:00:58 lr: 0.000100 loss: 83.0998 (115.2337) loss_classifier: 12.7831 (14.0151) loss_box_reg: 21.4768 (36.6713) loss_objectness: 29.0046 (44.1066) loss_rpn_box_reg: 10.4131 (20.4406) time: 0.8762 data: 0.0227 max mem: 6508 Epoch: [4] [ 50/104] eta: 0:00:49 lr: 0.000100 loss: 79.7844 (112.6534) loss_classifier: 12.8533 (14.6672) loss_box_reg: 15.9272 (32.9115) loss_objectness: 31.7846 (43.8854) loss_rpn_box_reg: 12.3448 (21.1893) time: 0.8691 data: 0.0226 max mem: 6508 Epoch: [4] [ 60/104] eta: 0:00:39 lr: 0.000100 loss: 88.3089 (110.2105) loss_classifier: 14.3843 (14.4013) loss_box_reg: 14.1213 (30.6656) loss_objectness: 37.6492 (44.1127) loss_rpn_box_reg: 14.8587 (21.0310) time: 0.8636 data: 0.0221 max mem: 6508 Epoch: [4] [ 70/104] eta: 0:00:30 lr: 0.000100 loss: 76.0205 (104.6302) loss_classifier: 5.5285 (12.7650) loss_box_reg: 4.2127 (26.5244) loss_objectness: 37.6492 (43.9946) loss_rpn_box_reg: 16.4864 (21.3462) time: 0.8680 data: 0.0243 max mem: 6508 Epoch: [4] [ 80/104] eta: 0:00:21 lr: 0.000100 loss: 60.9173 (99.6835) loss_classifier: 0.5171 (11.2307) loss_box_reg: 0.5805 (23.3846) loss_objectness: 37.0190 (44.0343) loss_rpn_box_reg: 17.7755 (21.0340) time: 0.8956 data: 0.0250 max mem: 6508 Epoch: [4] [ 90/104] eta: 0:00:12 lr: 0.000100 loss: 47.0268 (95.8740) loss_classifier: 0.2698 (10.0324) loss_box_reg: 0.3479 (20.8609) loss_objectness: 36.3957 (43.7518) loss_rpn_box_reg: 11.3258 (21.2289) time: 0.9245 data: 0.0208 max mem: 6508 Epoch: [4] [100/104] eta: 0:00:03 lr: 0.000100 loss: 46.4205 (92.1205) loss_classifier: 0.2538 (9.0769) loss_box_reg: 0.3883 (18.8635) loss_objectness: 34.8001 (43.2130) loss_rpn_box_reg: 10.7662 (20.9672) time: 0.9419 data: 0.0185 max mem: 6508 Epoch: [4] [103/104] eta: 0:00:00 lr: 0.000100 loss: 46.4205 (91.3613) loss_classifier: 0.2067 (8.8189) loss_box_reg: 0.3031 (18.3242) loss_objectness: 34.8001 (43.2987) loss_rpn_box_reg: 11.2495 (20.9195) time: 0.9473 data: 0.0181 max mem: 6508 Epoch: [4] Total time: 0:01:34 (0.9111 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:31 model_time: 0.4938 (0.4938) evaluator_time: 0.0210 (0.0210) time: 1.2012 data: 0.6711 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3945 (0.3990) evaluator_time: 0.0087 (0.0117) time: 0.4304 data: 0.0188 max mem: 6508 Test: Total time: 0:00:12 (0.4672 s / it) Averaged stats: model_time: 0.3945 (0.3990) evaluator_time: 0.0087 (0.0117) Accumulating evaluation results... DONE (t=0.10s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [5] [ 0/104] eta: 0:03:08 lr: 0.000100 loss: 20.0014 (20.0014) loss_classifier: 0.1053 (0.1053) loss_box_reg: 0.0933 (0.0933) loss_objectness: 17.1295 (17.1295) loss_rpn_box_reg: 2.6733 (2.6733) time: 1.8093 data: 0.7300 max mem: 6508 Epoch: [5] [ 10/104] eta: 0:01:41 lr: 0.000100 loss: 41.9883 (67.5856) loss_classifier: 0.2323 (0.4990) loss_box_reg: 0.5077 (0.8016) loss_objectness: 31.6889 (44.3222) loss_rpn_box_reg: 8.5974 (21.9628) time: 1.0764 data: 0.0864 max mem: 6508 Epoch: [5] [ 20/104] eta: 0:01:27 lr: 0.000100 loss: 41.9883 (74.8238) loss_classifier: 0.2920 (0.4492) loss_box_reg: 0.6207 (0.7669) loss_objectness: 31.6889 (48.9295) loss_rpn_box_reg: 9.0488 (24.6782) time: 1.0055 data: 0.0221 max mem: 6508 Epoch: [5] [ 30/104] eta: 0:01:15 lr: 0.000100 loss: 36.4438 (63.3653) loss_classifier: 0.2598 (0.4511) loss_box_reg: 0.5993 (0.7657) loss_objectness: 26.6947 (41.4684) loss_rpn_box_reg: 9.4150 (20.6801) time: 0.9988 data: 0.0209 max mem: 6508 Epoch: [5] [ 40/104] eta: 0:01:04 lr: 0.000100 loss: 35.3091 (63.8564) loss_classifier: 0.3117 (0.5346) loss_box_reg: 0.8865 (0.9180) loss_objectness: 24.7180 (41.4591) loss_rpn_box_reg: 8.4455 (20.9447) time: 0.9850 data: 0.0208 max mem: 6508 Epoch: [5] [ 50/104] eta: 0:00:54 lr: 0.000100 loss: 45.7666 (63.1234) loss_classifier: 0.8353 (0.7369) loss_box_reg: 0.7469 (0.8398) loss_objectness: 30.9653 (40.4953) loss_rpn_box_reg: 12.1707 (21.0514) time: 0.9794 data: 0.0232 max mem: 6508 Epoch: [5] [ 60/104] eta: 0:00:43 lr: 0.000100 loss: 47.3924 (63.5978) loss_classifier: 0.8437 (0.7450) loss_box_reg: 0.3043 (0.7829) loss_objectness: 32.3307 (41.0266) loss_rpn_box_reg: 13.1207 (21.0433) time: 0.9675 data: 0.0219 max mem: 6508 Epoch: [5] [ 70/104] eta: 0:00:33 lr: 0.000100 loss: 48.2683 (62.4787) loss_classifier: 0.7768 (0.7573) loss_box_reg: 0.5655 (0.8049) loss_objectness: 32.3307 (40.3854) loss_rpn_box_reg: 13.3730 (20.5311) time: 0.9623 data: 0.0232 max mem: 6508 Epoch: [5] [ 80/104] eta: 0:00:23 lr: 0.000100 loss: 47.3047 (60.7057) loss_classifier: 0.6391 (0.7672) loss_box_reg: 0.7198 (0.7969) loss_objectness: 31.1596 (39.3882) loss_rpn_box_reg: 13.4515 (19.7535) time: 0.9573 data: 0.0253 max mem: 6508 Epoch: [5] [ 90/104] eta: 0:00:13 lr: 0.000100 loss: 40.0958 (58.2975) loss_classifier: 0.5869 (0.7933) loss_box_reg: 0.6141 (0.8455) loss_objectness: 28.8788 (38.0051) loss_rpn_box_reg: 12.1153 (18.6536) time: 0.9632 data: 0.0246 max mem: 6508 Epoch: [5] [100/104] eta: 0:00:03 lr: 0.000100 loss: 40.0958 (57.9070) loss_classifier: 0.7374 (0.8084) loss_box_reg: 0.6148 (0.8468) loss_objectness: 26.4502 (37.8837) loss_rpn_box_reg: 9.1422 (18.3681) time: 0.9601 data: 0.0218 max mem: 6508 Epoch: [5] [103/104] eta: 0:00:00 lr: 0.000100 loss: 37.3095 (57.5049) loss_classifier: 0.7374 (0.8197) loss_box_reg: 0.6622 (0.8917) loss_objectness: 26.3762 (37.6363) loss_rpn_box_reg: 9.1422 (18.1572) time: 0.9572 data: 0.0222 max mem: 6508 Epoch: [5] Total time: 0:01:42 (0.9836 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.4740 (0.4740) evaluator_time: 0.0147 (0.0147) time: 1.2605 data: 0.7454 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3786 (0.3810) evaluator_time: 0.0057 (0.0071) time: 0.4114 data: 0.0203 max mem: 6508 Test: Total time: 0:00:11 (0.4494 s / it) Averaged stats: model_time: 0.3786 (0.3810) evaluator_time: 0.0057 (0.0071) Accumulating evaluation results... DONE (t=0.12s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [6] [ 0/104] eta: 0:03:57 lr: 0.000010 loss: 24.8814 (24.8814) loss_classifier: 0.8751 (0.8751) loss_box_reg: 1.3191 (1.3191) loss_objectness: 18.5028 (18.5028) loss_rpn_box_reg: 4.1844 (4.1844) time: 2.2849 data: 1.1360 max mem: 6508 Epoch: [6] [ 10/104] eta: 0:01:41 lr: 0.000010 loss: 37.0075 (51.6789) loss_classifier: 0.8751 (1.3198) loss_box_reg: 1.0455 (1.6272) loss_objectness: 23.6202 (29.5659) loss_rpn_box_reg: 12.6925 (19.1660) time: 1.0767 data: 0.1201 max mem: 6508 Epoch: [6] [ 20/104] eta: 0:01:25 lr: 0.000010 loss: 44.0930 (62.7234) loss_classifier: 0.8575 (1.1503) loss_box_reg: 0.8651 (1.4286) loss_objectness: 27.5672 (38.6000) loss_rpn_box_reg: 13.2039 (21.5444) time: 0.9558 data: 0.0206 max mem: 6508 Epoch: [6] [ 30/104] eta: 0:01:13 lr: 0.000010 loss: 57.8508 (66.9983) loss_classifier: 0.9510 (1.3345) loss_box_reg: 0.9510 (1.5951) loss_objectness: 35.4797 (41.0589) loss_rpn_box_reg: 15.4171 (23.0098) time: 0.9395 data: 0.0226 max mem: 6508 Epoch: [6] [ 40/104] eta: 0:01:01 lr: 0.000010 loss: 36.9282 (61.7737) loss_classifier: 1.2070 (1.3313) loss_box_reg: 0.8505 (1.5105) loss_objectness: 25.2796 (38.4129) loss_rpn_box_reg: 10.4051 (20.5190) time: 0.9082 data: 0.0216 max mem: 6508 Epoch: [6] [ 50/104] eta: 0:00:51 lr: 0.000010 loss: 32.7680 (58.1379) loss_classifier: 1.2070 (1.3677) loss_box_reg: 1.2339 (1.6008) loss_objectness: 21.0982 (36.3104) loss_rpn_box_reg: 5.5364 (18.8590) time: 0.8981 data: 0.0225 max mem: 6508 Epoch: [6] [ 60/104] eta: 0:00:41 lr: 0.000010 loss: 47.7588 (57.6452) loss_classifier: 1.3659 (1.3310) loss_box_reg: 1.7621 (1.6372) loss_objectness: 30.3292 (36.0883) loss_rpn_box_reg: 9.6681 (18.5887) time: 0.9035 data: 0.0237 max mem: 6508 Epoch: [6] [ 70/104] eta: 0:00:31 lr: 0.000010 loss: 51.2820 (58.2520) loss_classifier: 1.3659 (1.4285) loss_box_reg: 1.9540 (1.8565) loss_objectness: 30.8757 (36.1639) loss_rpn_box_reg: 14.9676 (18.8032) time: 0.8961 data: 0.0212 max mem: 6508 Epoch: [6] [ 80/104] eta: 0:00:22 lr: 0.000010 loss: 35.5225 (57.1742) loss_classifier: 1.2666 (1.4121) loss_box_reg: 1.8910 (1.8212) loss_objectness: 23.9973 (35.5701) loss_rpn_box_reg: 8.0572 (18.3709) time: 0.8954 data: 0.0219 max mem: 6508 Epoch: [6] [ 90/104] eta: 0:00:13 lr: 0.000010 loss: 50.4645 (59.3462) loss_classifier: 1.0201 (1.4707) loss_box_reg: 0.8960 (1.8823) loss_objectness: 31.3237 (37.0268) loss_rpn_box_reg: 11.1851 (18.9664) time: 0.9084 data: 0.0241 max mem: 6508 Epoch: [6] [100/104] eta: 0:00:03 lr: 0.000010 loss: 34.5830 (56.6741) loss_classifier: 1.1253 (1.4483) loss_box_reg: 1.0179 (1.9148) loss_objectness: 25.9233 (35.5759) loss_rpn_box_reg: 8.6166 (17.7352) time: 0.9026 data: 0.0213 max mem: 6508 Epoch: [6] [103/104] eta: 0:00:00 lr: 0.000010 loss: 34.5830 (56.2563) loss_classifier: 1.0971 (1.4783) loss_box_reg: 1.0179 (1.9213) loss_objectness: 25.9233 (35.3313) loss_rpn_box_reg: 7.3149 (17.5255) time: 0.9046 data: 0.0213 max mem: 6508 Epoch: [6] Total time: 0:01:36 (0.9286 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:45 model_time: 0.5394 (0.5394) evaluator_time: 0.0150 (0.0150) time: 1.7539 data: 1.1803 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3717 (0.3802) evaluator_time: 0.0048 (0.0061) time: 0.4023 data: 0.0195 max mem: 6508 Test: Total time: 0:00:12 (0.4624 s / it) Averaged stats: model_time: 0.3717 (0.3802) evaluator_time: 0.0048 (0.0061) Accumulating evaluation results... DONE (t=0.06s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [7] [ 0/104] eta: 0:03:51 lr: 0.000010 loss: 87.8664 (87.8664) loss_classifier: 0.7757 (0.7757) loss_box_reg: 0.3214 (0.3214) loss_objectness: 60.4727 (60.4727) loss_rpn_box_reg: 26.2966 (26.2966) time: 2.2264 data: 1.0773 max mem: 6508 Epoch: [7] [ 10/104] eta: 0:01:37 lr: 0.000010 loss: 47.3212 (64.1296) loss_classifier: 1.4586 (2.0382) loss_box_reg: 1.4097 (2.1041) loss_objectness: 27.7213 (40.2605) loss_rpn_box_reg: 14.9454 (19.7268) time: 1.0400 data: 0.1201 max mem: 6508 Epoch: [7] [ 20/104] eta: 0:01:22 lr: 0.000010 loss: 39.5393 (58.7766) loss_classifier: 1.2743 (2.0464) loss_box_reg: 1.6440 (2.6563) loss_objectness: 27.7213 (36.5018) loss_rpn_box_reg: 8.0800 (17.5721) time: 0.9220 data: 0.0242 max mem: 6508 Epoch: [7] [ 30/104] eta: 0:01:11 lr: 0.000010 loss: 38.5752 (53.8027) loss_classifier: 0.9598 (1.7169) loss_box_reg: 1.0913 (2.1371) loss_objectness: 28.3370 (34.1707) loss_rpn_box_reg: 8.0800 (15.7781) time: 0.9190 data: 0.0219 max mem: 6508 Epoch: [7] [ 40/104] eta: 0:01:00 lr: 0.000010 loss: 42.6587 (56.0816) loss_classifier: 0.9598 (1.6737) loss_box_reg: 0.6641 (2.0950) loss_objectness: 29.4402 (35.0810) loss_rpn_box_reg: 11.0174 (17.2319) time: 0.9084 data: 0.0201 max mem: 6508 Epoch: [7] [ 50/104] eta: 0:00:50 lr: 0.000010 loss: 50.4157 (58.5879) loss_classifier: 1.0065 (1.7349) loss_box_reg: 0.9827 (2.0586) loss_objectness: 33.9286 (36.9820) loss_rpn_box_reg: 15.4339 (17.8124) time: 0.8982 data: 0.0208 max mem: 6508 Epoch: [7] [ 60/104] eta: 0:00:40 lr: 0.000010 loss: 34.5905 (55.2556) loss_classifier: 0.7998 (1.6528) loss_box_reg: 0.9682 (1.9844) loss_objectness: 24.7950 (34.9693) loss_rpn_box_reg: 7.9839 (16.6490) time: 0.8985 data: 0.0214 max mem: 6508 Epoch: [7] [ 70/104] eta: 0:00:31 lr: 0.000010 loss: 29.1215 (55.7689) loss_classifier: 0.7629 (1.6086) loss_box_reg: 1.5312 (2.0877) loss_objectness: 19.9744 (34.9326) loss_rpn_box_reg: 5.5967 (17.1400) time: 0.9043 data: 0.0213 max mem: 6508 Epoch: [7] [ 80/104] eta: 0:00:22 lr: 0.000010 loss: 35.2129 (55.1393) loss_classifier: 0.9716 (1.5208) loss_box_reg: 1.5312 (2.0082) loss_objectness: 23.3236 (34.7006) loss_rpn_box_reg: 6.5975 (16.9096) time: 0.9068 data: 0.0211 max mem: 6508 Epoch: [7] [ 90/104] eta: 0:00:12 lr: 0.000010 loss: 37.3594 (55.3226) loss_classifier: 0.9789 (1.5148) loss_box_reg: 1.5428 (2.0304) loss_objectness: 25.0347 (34.6903) loss_rpn_box_reg: 10.9191 (17.0871) time: 0.8987 data: 0.0203 max mem: 6508 Epoch: [7] [100/104] eta: 0:00:03 lr: 0.000010 loss: 38.2908 (55.4929) loss_classifier: 1.1156 (1.5634) loss_box_reg: 1.6116 (2.0739) loss_objectness: 23.8722 (34.6085) loss_rpn_box_reg: 8.4591 (17.2470) time: 0.8915 data: 0.0194 max mem: 6508 Epoch: [7] [103/104] eta: 0:00:00 lr: 0.000010 loss: 48.2291 (56.1940) loss_classifier: 1.1156 (1.5593) loss_box_reg: 1.1863 (2.0916) loss_objectness: 25.4885 (34.9871) loss_rpn_box_reg: 11.4533 (17.5560) time: 0.8940 data: 0.0189 max mem: 6508 Epoch: [7] Total time: 0:01:35 (0.9197 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.4474 (0.4474) evaluator_time: 0.0151 (0.0151) time: 1.2321 data: 0.7581 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3696 (0.3729) evaluator_time: 0.0054 (0.0060) time: 0.4167 data: 0.0345 max mem: 6508 Test: Total time: 0:00:11 (0.4516 s / it) Averaged stats: model_time: 0.3696 (0.3729) evaluator_time: 0.0054 (0.0060) Accumulating evaluation results... DONE (t=0.11s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [8] [ 0/104] eta: 0:03:19 lr: 0.000010 loss: 15.9957 (15.9957) loss_classifier: 0.3973 (0.3973) loss_box_reg: 0.4962 (0.4962) loss_objectness: 11.7689 (11.7689) loss_rpn_box_reg: 3.3333 (3.3333) time: 1.9187 data: 0.9615 max mem: 6508 Epoch: [8] [ 10/104] eta: 0:01:35 lr: 0.000010 loss: 32.1010 (56.6887) loss_classifier: 1.5712 (1.5366) loss_box_reg: 1.5819 (2.2572) loss_objectness: 18.2520 (32.7409) loss_rpn_box_reg: 7.2458 (20.1540) time: 1.0139 data: 0.1060 max mem: 6508 Epoch: [8] [ 20/104] eta: 0:01:22 lr: 0.000010 loss: 32.2809 (49.5535) loss_classifier: 0.6473 (1.2144) loss_box_reg: 1.5546 (2.0191) loss_objectness: 22.6618 (30.3870) loss_rpn_box_reg: 6.8732 (15.9329) time: 0.9337 data: 0.0210 max mem: 6508 Epoch: [8] [ 30/104] eta: 0:01:11 lr: 0.000010 loss: 33.1148 (50.5814) loss_classifier: 0.6470 (1.1718) loss_box_reg: 1.1354 (1.9984) loss_objectness: 24.9701 (31.8476) loss_rpn_box_reg: 5.7260 (15.5636) time: 0.9408 data: 0.0207 max mem: 6508 Epoch: [8] [ 40/104] eta: 0:01:01 lr: 0.000010 loss: 38.0942 (49.5056) loss_classifier: 0.7300 (1.1300) loss_box_reg: 1.1214 (1.8952) loss_objectness: 25.0311 (30.6048) loss_rpn_box_reg: 8.7146 (15.8756) time: 0.9374 data: 0.0220 max mem: 6508 Epoch: [8] [ 50/104] eta: 0:00:51 lr: 0.000010 loss: 36.8604 (51.6394) loss_classifier: 0.9232 (1.1640) loss_box_reg: 1.1698 (1.8768) loss_objectness: 24.8990 (32.1092) loss_rpn_box_reg: 8.7146 (16.4894) time: 0.9236 data: 0.0228 max mem: 6508 Epoch: [8] [ 60/104] eta: 0:00:41 lr: 0.000010 loss: 44.3082 (56.5265) loss_classifier: 0.9232 (1.1949) loss_box_reg: 1.1370 (1.9806) loss_objectness: 30.9550 (35.3398) loss_rpn_box_reg: 9.3121 (18.0113) time: 0.8972 data: 0.0205 max mem: 6508 Epoch: [8] [ 70/104] eta: 0:00:31 lr: 0.000010 loss: 56.3628 (57.0742) loss_classifier: 1.0805 (1.2302) loss_box_reg: 1.1370 (2.1024) loss_objectness: 33.9098 (35.5870) loss_rpn_box_reg: 16.4104 (18.1545) time: 0.8856 data: 0.0213 max mem: 6508 Epoch: [8] [ 80/104] eta: 0:00:22 lr: 0.000010 loss: 39.0102 (54.4213) loss_classifier: 1.2735 (1.3293) loss_box_reg: 1.3677 (2.2665) loss_objectness: 26.5310 (33.9587) loss_rpn_box_reg: 7.9361 (16.8668) time: 0.8897 data: 0.0229 max mem: 6508 Epoch: [8] [ 90/104] eta: 0:00:12 lr: 0.000010 loss: 34.5572 (54.0793) loss_classifier: 0.9454 (1.2863) loss_box_reg: 1.1661 (2.1681) loss_objectness: 22.4779 (33.4381) loss_rpn_box_reg: 8.1952 (17.1868) time: 0.8948 data: 0.0215 max mem: 6508 Epoch: [8] [100/104] eta: 0:00:03 lr: 0.000010 loss: 49.5937 (55.8098) loss_classifier: 0.6847 (1.3095) loss_box_reg: 1.5164 (2.2174) loss_objectness: 33.7459 (34.5916) loss_rpn_box_reg: 12.6412 (17.6912) time: 0.8954 data: 0.0203 max mem: 6508 Epoch: [8] [103/104] eta: 0:00:00 lr: 0.000010 loss: 53.0680 (55.5054) loss_classifier: 0.7807 (1.3054) loss_box_reg: 2.0435 (2.3042) loss_objectness: 35.3156 (34.3672) loss_rpn_box_reg: 15.0755 (17.5286) time: 0.8918 data: 0.0191 max mem: 6508 Epoch: [8] Total time: 0:01:35 (0.9214 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:34 model_time: 0.4613 (0.4613) evaluator_time: 0.0122 (0.0122) time: 1.3162 data: 0.8309 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3731 (0.3763) evaluator_time: 0.0047 (0.0054) time: 0.4072 data: 0.0207 max mem: 6508 Test: Total time: 0:00:11 (0.4443 s / it) Averaged stats: model_time: 0.3731 (0.3763) evaluator_time: 0.0047 (0.0054) Accumulating evaluation results... DONE (t=0.05s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [9] [ 0/104] eta: 0:03:19 lr: 0.000001 loss: 55.8726 (55.8726) loss_classifier: 0.9155 (0.9155) loss_box_reg: 0.7995 (0.7995) loss_objectness: 31.7795 (31.7795) loss_rpn_box_reg: 22.3781 (22.3781) time: 1.9175 data: 0.9353 max mem: 6508 Epoch: [9] [ 10/104] eta: 0:01:35 lr: 0.000001 loss: 36.0210 (36.4639) loss_classifier: 0.7631 (0.9774) loss_box_reg: 0.7818 (1.5536) loss_objectness: 24.9991 (24.2409) loss_rpn_box_reg: 5.7209 (9.6920) time: 1.0133 data: 0.0998 max mem: 6508 Epoch: [9] [ 20/104] eta: 0:01:22 lr: 0.000001 loss: 36.0210 (46.6495) loss_classifier: 0.5396 (0.9421) loss_box_reg: 0.4097 (1.3542) loss_objectness: 24.9991 (30.2645) loss_rpn_box_reg: 5.7209 (14.0887) time: 0.9318 data: 0.0175 max mem: 6508 Epoch: [9] [ 30/104] eta: 0:01:11 lr: 0.000001 loss: 47.9818 (50.8850) loss_classifier: 0.5169 (1.0119) loss_box_reg: 0.7757 (1.6858) loss_objectness: 33.5256 (32.1530) loss_rpn_box_reg: 10.3965 (16.0343) time: 0.9348 data: 0.0207 max mem: 6508 Epoch: [9] [ 40/104] eta: 0:01:00 lr: 0.000001 loss: 56.1572 (58.4918) loss_classifier: 0.6345 (1.1533) loss_box_reg: 1.4540 (2.2561) loss_objectness: 39.3350 (36.7060) loss_rpn_box_reg: 10.8813 (18.3764) time: 0.9227 data: 0.0226 max mem: 6508 Epoch: [9] [ 50/104] eta: 0:00:50 lr: 0.000001 loss: 40.9915 (58.5107) loss_classifier: 1.1223 (1.3139) loss_box_reg: 1.5430 (2.4593) loss_objectness: 26.8529 (36.6794) loss_rpn_box_reg: 7.0965 (18.0581) time: 0.8986 data: 0.0208 max mem: 6508 Epoch: [9] [ 60/104] eta: 0:00:40 lr: 0.000001 loss: 40.7521 (59.0624) loss_classifier: 0.7274 (1.2571) loss_box_reg: 2.0573 (2.4919) loss_objectness: 25.3522 (36.6843) loss_rpn_box_reg: 6.5220 (18.6290) time: 0.8901 data: 0.0210 max mem: 6508 Epoch: [9] [ 70/104] eta: 0:00:31 lr: 0.000001 loss: 41.9558 (57.6186) loss_classifier: 0.5666 (1.2584) loss_box_reg: 1.7006 (2.4362) loss_objectness: 29.7488 (35.7220) loss_rpn_box_reg: 6.8678 (18.2021) time: 0.8941 data: 0.0224 max mem: 6508 Epoch: [9] [ 80/104] eta: 0:00:22 lr: 0.000001 loss: 38.1649 (56.1024) loss_classifier: 0.9652 (1.2393) loss_box_reg: 1.7006 (2.3581) loss_objectness: 29.1210 (35.0864) loss_rpn_box_reg: 7.8529 (17.4186) time: 0.8848 data: 0.0208 max mem: 6508 Epoch: [9] [ 90/104] eta: 0:00:12 lr: 0.000001 loss: 39.4567 (55.4460) loss_classifier: 0.6309 (1.1702) loss_box_reg: 0.5337 (2.2205) loss_objectness: 29.2802 (34.6866) loss_rpn_box_reg: 9.7066 (17.3688) time: 0.8894 data: 0.0209 max mem: 6508 Epoch: [9] [100/104] eta: 0:00:03 lr: 0.000001 loss: 46.5877 (55.7092) loss_classifier: 0.5047 (1.2301) loss_box_reg: 0.5314 (2.2278) loss_objectness: 24.7870 (34.5281) loss_rpn_box_reg: 12.9687 (17.7232) time: 0.8960 data: 0.0205 max mem: 6508 Epoch: [9] [103/104] eta: 0:00:00 lr: 0.000001 loss: 45.1522 (54.8422) loss_classifier: 0.5047 (1.2155) loss_box_reg: 0.5314 (2.2548) loss_objectness: 22.5050 (33.9452) loss_rpn_box_reg: 10.2175 (17.4267) time: 0.8943 data: 0.0194 max mem: 6508 Epoch: [9] Total time: 0:01:35 (0.9157 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.5010 (0.5010) evaluator_time: 0.0206 (0.0206) time: 1.2450 data: 0.6964 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3725 (0.3774) evaluator_time: 0.0049 (0.0061) time: 0.4074 data: 0.0213 max mem: 6508 Test: Total time: 0:00:11 (0.4435 s / it) Averaged stats: model_time: 0.3725 (0.3774) evaluator_time: 0.0049 (0.0061) Accumulating evaluation results... DONE (t=0.06s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [10] [ 0/104] eta: 0:03:15 lr: 0.000001 loss: 136.4492 (136.4492) loss_classifier: 0.7131 (0.7131) loss_box_reg: 1.0747 (1.0747) loss_objectness: 74.9324 (74.9324) loss_rpn_box_reg: 59.7291 (59.7291) time: 1.8833 data: 0.9262 max mem: 6508 Epoch: [10] [ 10/104] eta: 0:01:35 lr: 0.000001 loss: 33.3319 (60.0367) loss_classifier: 1.4028 (1.6601) loss_box_reg: 2.5008 (2.2897) loss_objectness: 25.7583 (35.5108) loss_rpn_box_reg: 6.7655 (20.5761) time: 1.0110 data: 0.0995 max mem: 6508 Epoch: [10] [ 20/104] eta: 0:01:21 lr: 0.000001 loss: 33.3319 (52.6729) loss_classifier: 0.7580 (1.3099) loss_box_reg: 1.2173 (2.0461) loss_objectness: 24.6153 (31.5986) loss_rpn_box_reg: 7.6742 (17.7184) time: 0.9304 data: 0.0183 max mem: 6508 Epoch: [10] [ 30/104] eta: 0:01:11 lr: 0.000001 loss: 37.1256 (56.9344) loss_classifier: 0.6061 (1.1171) loss_box_reg: 0.7630 (1.6936) loss_objectness: 24.8843 (34.7807) loss_rpn_box_reg: 11.1457 (19.3429) time: 0.9365 data: 0.0201 max mem: 6508 Epoch: [10] [ 40/104] eta: 0:01:00 lr: 0.000001 loss: 30.8479 (54.6998) loss_classifier: 0.5191 (1.1215) loss_box_reg: 1.5854 (1.9849) loss_objectness: 24.2222 (33.4807) loss_rpn_box_reg: 8.9800 (18.1127) time: 0.9287 data: 0.0233 max mem: 6508 Epoch: [10] [ 50/104] eta: 0:00:50 lr: 0.000001 loss: 50.0852 (59.1014) loss_classifier: 0.4484 (0.9894) loss_box_reg: 1.1914 (1.7593) loss_objectness: 34.0017 (36.5236) loss_rpn_box_reg: 9.6337 (19.8292) time: 0.9103 data: 0.0237 max mem: 6508 Epoch: [10] [ 60/104] eta: 0:00:41 lr: 0.000001 loss: 40.0831 (56.1553) loss_classifier: 0.4169 (1.0080) loss_box_reg: 0.9465 (1.8049) loss_objectness: 31.6625 (35.0517) loss_rpn_box_reg: 11.9654 (18.2907) time: 0.8914 data: 0.0217 max mem: 6508 Epoch: [10] [ 70/104] eta: 0:00:31 lr: 0.000001 loss: 40.0831 (57.7541) loss_classifier: 0.6742 (1.1186) loss_box_reg: 1.9267 (2.2065) loss_objectness: 26.0857 (35.8218) loss_rpn_box_reg: 11.1730 (18.6072) time: 0.8808 data: 0.0212 max mem: 6508 Epoch: [10] [ 80/104] eta: 0:00:22 lr: 0.000001 loss: 43.0268 (56.7424) loss_classifier: 0.7473 (1.0691) loss_box_reg: 1.3503 (2.1036) loss_objectness: 29.3088 (35.4298) loss_rpn_box_reg: 10.3651 (18.1399) time: 0.8871 data: 0.0218 max mem: 6508 Epoch: [10] [ 90/104] eta: 0:00:12 lr: 0.000001 loss: 37.6172 (55.5382) loss_classifier: 0.6455 (1.0569) loss_box_reg: 0.8860 (2.0527) loss_objectness: 25.9196 (34.2333) loss_rpn_box_reg: 8.6694 (18.1952) time: 0.8944 data: 0.0222 max mem: 6508 Epoch: [10] [100/104] eta: 0:00:03 lr: 0.000001 loss: 34.2634 (55.1000) loss_classifier: 0.7205 (1.1929) loss_box_reg: 1.0591 (2.2203) loss_objectness: 25.9196 (33.9396) loss_rpn_box_reg: 8.3527 (17.7472) time: 0.8922 data: 0.0199 max mem: 6508 Epoch: [10] [103/104] eta: 0:00:00 lr: 0.000001 loss: 38.6925 (54.6933) loss_classifier: 1.0530 (1.1821) loss_box_reg: 2.4034 (2.2524) loss_objectness: 26.8310 (33.7915) loss_rpn_box_reg: 8.3527 (17.4673) time: 0.8956 data: 0.0196 max mem: 6508 Epoch: [10] Total time: 0:01:35 (0.9175 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:47 model_time: 0.6206 (0.6206) evaluator_time: 0.0600 (0.0600) time: 1.8325 data: 1.0885 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3731 (0.3831) evaluator_time: 0.0041 (0.0069) time: 0.4015 data: 0.0178 max mem: 6508 Test: Total time: 0:00:12 (0.4636 s / it) Averaged stats: model_time: 0.3731 (0.3831) evaluator_time: 0.0041 (0.0069) Accumulating evaluation results... DONE (t=0.06s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [11] [ 0/104] eta: 0:03:00 lr: 0.000001 loss: 52.8316 (52.8316) loss_classifier: 0.1815 (0.1815) loss_box_reg: 0.4922 (0.4922) loss_objectness: 30.7382 (30.7382) loss_rpn_box_reg: 21.4197 (21.4197) time: 1.7318 data: 0.5542 max mem: 6508 Epoch: [11] [ 10/104] eta: 0:01:34 lr: 0.000001 loss: 52.8316 (57.8617) loss_classifier: 0.5240 (0.6211) loss_box_reg: 0.9764 (1.1655) loss_objectness: 33.1904 (37.8400) loss_rpn_box_reg: 15.1997 (18.2351) time: 1.0077 data: 0.0694 max mem: 6508 Epoch: [11] [ 20/104] eta: 0:01:21 lr: 0.000001 loss: 43.8346 (53.9513) loss_classifier: 0.5493 (1.0243) loss_box_reg: 1.1809 (1.9616) loss_objectness: 29.4245 (35.0679) loss_rpn_box_reg: 9.4829 (15.8975) time: 0.9359 data: 0.0206 max mem: 6508 Epoch: [11] [ 30/104] eta: 0:01:10 lr: 0.000001 loss: 37.5880 (53.6489) loss_classifier: 0.9131 (1.1859) loss_box_reg: 1.4377 (2.3371) loss_objectness: 24.8980 (34.1686) loss_rpn_box_reg: 8.3418 (15.9573) time: 0.9246 data: 0.0194 max mem: 6508 Epoch: [11] [ 40/104] eta: 0:01:00 lr: 0.000001 loss: 37.4579 (51.5240) loss_classifier: 1.0893 (1.1295) loss_box_reg: 1.4377 (2.1583) loss_objectness: 24.4681 (32.9490) loss_rpn_box_reg: 8.4908 (15.2872) time: 0.9086 data: 0.0196 max mem: 6508 Epoch: [11] [ 50/104] eta: 0:00:50 lr: 0.000001 loss: 40.1210 (55.8891) loss_classifier: 0.6040 (1.0525) loss_box_reg: 1.2593 (2.0721) loss_objectness: 31.2911 (35.8685) loss_rpn_box_reg: 8.4908 (16.8960) time: 0.8972 data: 0.0209 max mem: 6508 Epoch: [11] [ 60/104] eta: 0:00:40 lr: 0.000001 loss: 42.8720 (54.6735) loss_classifier: 0.5619 (0.9888) loss_box_reg: 1.2123 (1.9293) loss_objectness: 33.0219 (34.9984) loss_rpn_box_reg: 13.5257 (16.7570) time: 0.8877 data: 0.0203 max mem: 6508 Epoch: [11] [ 70/104] eta: 0:00:31 lr: 0.000001 loss: 47.5396 (54.0339) loss_classifier: 0.5431 (0.9441) loss_box_reg: 0.9506 (1.9152) loss_objectness: 28.7603 (34.4323) loss_rpn_box_reg: 14.2710 (16.7423) time: 0.8907 data: 0.0220 max mem: 6508 Epoch: [11] [ 80/104] eta: 0:00:21 lr: 0.000001 loss: 36.6762 (55.7692) loss_classifier: 0.7054 (1.0968) loss_box_reg: 1.4827 (2.0605) loss_objectness: 23.4399 (35.2508) loss_rpn_box_reg: 6.7993 (17.3611) time: 0.8920 data: 0.0228 max mem: 6508 Epoch: [11] [ 90/104] eta: 0:00:12 lr: 0.000001 loss: 36.6762 (54.3036) loss_classifier: 0.7671 (1.0819) loss_box_reg: 1.1549 (1.9902) loss_objectness: 23.1004 (34.2691) loss_rpn_box_reg: 6.8313 (16.9623) time: 0.8888 data: 0.0204 max mem: 6508 Epoch: [11] [100/104] eta: 0:00:03 lr: 0.000001 loss: 38.1807 (54.7137) loss_classifier: 0.7514 (1.1458) loss_box_reg: 0.8284 (2.1847) loss_objectness: 23.5308 (33.9078) loss_rpn_box_reg: 13.1102 (17.4754) time: 0.8988 data: 0.0205 max mem: 6508 Epoch: [11] [103/104] eta: 0:00:00 lr: 0.000001 loss: 41.9239 (54.5535) loss_classifier: 0.7198 (1.1400) loss_box_reg: 0.8284 (2.1824) loss_objectness: 27.0699 (33.7869) loss_rpn_box_reg: 13.1102 (17.4442) time: 0.9017 data: 0.0202 max mem: 6508 Epoch: [11] Total time: 0:01:35 (0.9137 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:29 model_time: 0.4388 (0.4388) evaluator_time: 0.0286 (0.0286) time: 1.1380 data: 0.6646 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3735 (0.3746) evaluator_time: 0.0049 (0.0061) time: 0.4048 data: 0.0209 max mem: 6508 Test: Total time: 0:00:11 (0.4384 s / it) Averaged stats: model_time: 0.3735 (0.3746) evaluator_time: 0.0049 (0.0061) Accumulating evaluation results... DONE (t=0.06s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [12] [ 0/104] eta: 0:02:59 lr: 0.000000 loss: 22.1330 (22.1330) loss_classifier: 0.2661 (0.2661) loss_box_reg: 0.2869 (0.2869) loss_objectness: 16.8623 (16.8623) loss_rpn_box_reg: 4.7176 (4.7176) time: 1.7247 data: 0.7689 max mem: 6508 Epoch: [12] [ 10/104] eta: 0:01:33 lr: 0.000000 loss: 41.8287 (56.1894) loss_classifier: 0.4315 (1.0307) loss_box_reg: 0.7968 (1.7654) loss_objectness: 28.2230 (36.2898) loss_rpn_box_reg: 7.5126 (17.1035) time: 0.9989 data: 0.0867 max mem: 6508 Epoch: [12] [ 20/104] eta: 0:01:21 lr: 0.000000 loss: 32.3787 (46.6581) loss_classifier: 0.4703 (0.8296) loss_box_reg: 0.9784 (1.4492) loss_objectness: 22.4661 (29.9172) loss_rpn_box_reg: 7.2703 (14.4621) time: 0.9285 data: 0.0205 max mem: 6508 Epoch: [12] [ 30/104] eta: 0:01:10 lr: 0.000000 loss: 30.8075 (51.9045) loss_classifier: 0.6102 (0.9255) loss_box_reg: 1.3047 (1.7918) loss_objectness: 22.5891 (33.4390) loss_rpn_box_reg: 7.9838 (15.7482) time: 0.9170 data: 0.0207 max mem: 6508 Epoch: [12] [ 40/104] eta: 0:00:59 lr: 0.000000 loss: 36.1320 (51.0136) loss_classifier: 0.5346 (0.9110) loss_box_reg: 0.4441 (1.6896) loss_objectness: 24.0349 (32.7051) loss_rpn_box_reg: 8.2391 (15.7079) time: 0.9038 data: 0.0200 max mem: 6508 Epoch: [12] [ 50/104] eta: 0:00:50 lr: 0.000000 loss: 38.1420 (50.6265) loss_classifier: 0.3968 (0.8540) loss_box_reg: 0.6667 (1.6038) loss_objectness: 23.1181 (32.4891) loss_rpn_box_reg: 9.0442 (15.6795) time: 0.8978 data: 0.0205 max mem: 6508 Epoch: [12] [ 60/104] eta: 0:00:40 lr: 0.000000 loss: 47.5172 (51.1805) loss_classifier: 0.4551 (0.8505) loss_box_reg: 0.6767 (1.6152) loss_objectness: 23.1181 (32.7655) loss_rpn_box_reg: 16.0064 (15.9494) time: 0.8884 data: 0.0198 max mem: 6508 Epoch: [12] [ 70/104] eta: 0:00:31 lr: 0.000000 loss: 39.2870 (50.6416) loss_classifier: 0.6406 (0.8506) loss_box_reg: 0.7314 (1.6614) loss_objectness: 28.4020 (32.2610) loss_rpn_box_reg: 9.2286 (15.8686) time: 0.8869 data: 0.0220 max mem: 6508 Epoch: [12] [ 80/104] eta: 0:00:21 lr: 0.000000 loss: 39.6601 (51.5920) loss_classifier: 0.7210 (0.9401) loss_box_reg: 2.0792 (1.8347) loss_objectness: 28.3379 (32.6720) loss_rpn_box_reg: 9.5574 (16.1453) time: 0.8899 data: 0.0229 max mem: 6508 Epoch: [12] [ 90/104] eta: 0:00:12 lr: 0.000000 loss: 44.6805 (53.8990) loss_classifier: 0.7210 (1.0102) loss_box_reg: 1.5198 (1.8423) loss_objectness: 28.3379 (33.5128) loss_rpn_box_reg: 10.2014 (17.5338) time: 0.8901 data: 0.0208 max mem: 6508 Epoch: [12] [100/104] eta: 0:00:03 lr: 0.000000 loss: 42.8190 (53.3576) loss_classifier: 0.4876 (1.0451) loss_box_reg: 0.6095 (1.9236) loss_objectness: 29.2609 (33.2374) loss_rpn_box_reg: 9.7693 (17.1515) time: 0.8921 data: 0.0200 max mem: 6508 Epoch: [12] [103/104] eta: 0:00:00 lr: 0.000000 loss: 42.8190 (53.7609) loss_classifier: 0.7151 (1.0634) loss_box_reg: 0.6377 (1.9176) loss_objectness: 29.2609 (33.3749) loss_rpn_box_reg: 9.6603 (17.4049) time: 0.8897 data: 0.0189 max mem: 6508 Epoch: [12] Total time: 0:01:34 (0.9093 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:31 model_time: 0.4823 (0.4823) evaluator_time: 0.0238 (0.0238) time: 1.2010 data: 0.6737 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3699 (0.3739) evaluator_time: 0.0054 (0.0063) time: 0.4055 data: 0.0223 max mem: 6508 Test: Total time: 0:00:11 (0.4382 s / it) Averaged stats: model_time: 0.3699 (0.3739) evaluator_time: 0.0054 (0.0063) Accumulating evaluation results... DONE (t=0.06s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [13] [ 0/104] eta: 0:03:01 lr: 0.000000 loss: 44.5011 (44.5011) loss_classifier: 2.7536 (2.7536) loss_box_reg: 10.0774 (10.0774) loss_objectness: 26.2172 (26.2172) loss_rpn_box_reg: 5.4529 (5.4529) time: 1.7486 data: 0.8313 max mem: 6508 Epoch: [13] [ 10/104] eta: 0:01:33 lr: 0.000000 loss: 44.2257 (53.2623) loss_classifier: 0.5256 (1.0915) loss_box_reg: 1.6258 (2.8355) loss_objectness: 27.6522 (35.1720) loss_rpn_box_reg: 8.7917 (14.1633) time: 0.9956 data: 0.0903 max mem: 6508 Epoch: [13] [ 20/104] eta: 0:01:21 lr: 0.000000 loss: 44.2257 (56.4916) loss_classifier: 0.7182 (1.4858) loss_box_reg: 1.5482 (2.8480) loss_objectness: 29.7883 (36.3452) loss_rpn_box_reg: 12.7240 (15.8126) time: 0.9343 data: 0.0204 max mem: 6508 Epoch: [13] [ 30/104] eta: 0:01:11 lr: 0.000000 loss: 39.6630 (55.2804) loss_classifier: 0.7264 (1.1933) loss_box_reg: 0.8544 (2.3042) loss_objectness: 26.7828 (35.6702) loss_rpn_box_reg: 14.2219 (16.1126) time: 0.9396 data: 0.0246 max mem: 6508 Epoch: [13] [ 40/104] eta: 0:01:00 lr: 0.000000 loss: 39.6630 (52.4549) loss_classifier: 0.5614 (1.1145) loss_box_reg: 0.7318 (2.2849) loss_objectness: 26.6561 (33.7647) loss_rpn_box_reg: 10.6641 (15.2909) time: 0.9201 data: 0.0227 max mem: 6508 Epoch: [13] [ 50/104] eta: 0:00:50 lr: 0.000000 loss: 37.3944 (51.5612) loss_classifier: 0.7112 (1.1365) loss_box_reg: 1.7637 (2.2915) loss_objectness: 24.0970 (32.3410) loss_rpn_box_reg: 9.6952 (15.7922) time: 0.9014 data: 0.0206 max mem: 6508 Epoch: [13] [ 60/104] eta: 0:00:40 lr: 0.000000 loss: 37.3944 (54.3545) loss_classifier: 0.8154 (1.0958) loss_box_reg: 1.9316 (2.2330) loss_objectness: 24.1391 (33.7685) loss_rpn_box_reg: 9.6952 (17.2571) time: 0.8955 data: 0.0210 max mem: 6508 Epoch: [13] [ 70/104] eta: 0:00:31 lr: 0.000000 loss: 48.4363 (55.8690) loss_classifier: 0.8154 (1.3169) loss_box_reg: 1.5628 (2.5893) loss_objectness: 28.8844 (34.0468) loss_rpn_box_reg: 10.0771 (17.9160) time: 0.8865 data: 0.0207 max mem: 6508 Epoch: [13] [ 80/104] eta: 0:00:22 lr: 0.000000 loss: 42.7073 (56.7093) loss_classifier: 0.7326 (1.2523) loss_box_reg: 1.3432 (2.4407) loss_objectness: 28.8844 (34.7017) loss_rpn_box_reg: 11.1174 (18.3146) time: 0.8817 data: 0.0203 max mem: 6508 Epoch: [13] [ 90/104] eta: 0:00:12 lr: 0.000000 loss: 35.5755 (55.1026) loss_classifier: 0.7326 (1.2471) loss_box_reg: 1.3305 (2.4795) loss_objectness: 24.4649 (33.9262) loss_rpn_box_reg: 6.0084 (17.4499) time: 0.8874 data: 0.0212 max mem: 6508 Epoch: [13] [100/104] eta: 0:00:03 lr: 0.000000 loss: 38.7166 (54.9869) loss_classifier: 0.6136 (1.1830) loss_box_reg: 0.5408 (2.3141) loss_objectness: 25.9364 (33.8751) loss_rpn_box_reg: 6.3024 (17.6147) time: 0.8934 data: 0.0216 max mem: 6508 Epoch: [13] [103/104] eta: 0:00:00 lr: 0.000000 loss: 38.7166 (54.6361) loss_classifier: 0.5983 (1.1788) loss_box_reg: 0.5408 (2.3405) loss_objectness: 25.9364 (33.6802) loss_rpn_box_reg: 8.0078 (17.4366) time: 0.8947 data: 0.0209 max mem: 6508 Epoch: [13] Total time: 0:01:35 (0.9147 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:38 model_time: 0.5600 (0.5600) evaluator_time: 0.0904 (0.0904) time: 1.4916 data: 0.8302 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3702 (0.3784) evaluator_time: 0.0042 (0.0199) time: 0.4151 data: 0.0197 max mem: 6508 Test: Total time: 0:00:12 (0.4646 s / it) Averaged stats: model_time: 0.3702 (0.3784) evaluator_time: 0.0042 (0.0199) Accumulating evaluation results... DONE (t=0.06s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [14] [ 0/104] eta: 0:02:57 lr: 0.000000 loss: 7.2644 (7.2644) loss_classifier: 0.4406 (0.4406) loss_box_reg: 1.2378 (1.2378) loss_objectness: 4.4630 (4.4630) loss_rpn_box_reg: 1.1230 (1.1230) time: 1.7115 data: 0.7214 max mem: 6508 Epoch: [14] [ 10/104] eta: 0:01:32 lr: 0.000000 loss: 40.6111 (41.7780) loss_classifier: 0.4624 (1.8352) loss_box_reg: 1.9378 (3.0898) loss_objectness: 30.6523 (27.2508) loss_rpn_box_reg: 9.4268 (9.6022) time: 0.9852 data: 0.0847 max mem: 6508 Epoch: [14] [ 20/104] eta: 0:01:20 lr: 0.000000 loss: 41.9498 (46.2010) loss_classifier: 0.5975 (1.4174) loss_box_reg: 1.6083 (2.8503) loss_objectness: 31.3423 (29.6717) loss_rpn_box_reg: 9.5961 (12.2616) time: 0.9239 data: 0.0220 max mem: 6508 Epoch: [14] [ 30/104] eta: 0:01:10 lr: 0.000000 loss: 44.8585 (49.7299) loss_classifier: 0.9981 (1.4701) loss_box_reg: 1.9661 (3.1523) loss_objectness: 29.2475 (30.8669) loss_rpn_box_reg: 12.0252 (14.2407) time: 0.9251 data: 0.0211 max mem: 6508 Epoch: [14] [ 40/104] eta: 0:00:59 lr: 0.000000 loss: 54.1296 (55.8704) loss_classifier: 0.9576 (1.2918) loss_box_reg: 1.7528 (2.7358) loss_objectness: 33.1939 (34.6591) loss_rpn_box_reg: 14.3342 (17.1836) time: 0.9105 data: 0.0196 max mem: 6508 Epoch: [14] [ 50/104] eta: 0:00:50 lr: 0.000000 loss: 45.0768 (54.3786) loss_classifier: 0.4658 (1.1630) loss_box_reg: 0.6312 (2.4449) loss_objectness: 29.7384 (34.0258) loss_rpn_box_reg: 12.8331 (16.7449) time: 0.9031 data: 0.0222 max mem: 6508 Epoch: [14] [ 60/104] eta: 0:00:40 lr: 0.000000 loss: 36.1728 (52.7044) loss_classifier: 0.5545 (1.1902) loss_box_reg: 1.5292 (2.4030) loss_objectness: 26.3899 (33.0513) loss_rpn_box_reg: 8.3081 (16.0599) time: 0.8876 data: 0.0215 max mem: 6508 Epoch: [14] [ 70/104] eta: 0:00:31 lr: 0.000000 loss: 41.8349 (55.4212) loss_classifier: 0.5278 (1.1009) loss_box_reg: 0.6700 (2.2442) loss_objectness: 26.3899 (34.4498) loss_rpn_box_reg: 8.2428 (17.6263) time: 0.8831 data: 0.0212 max mem: 6508 Epoch: [14] [ 80/104] eta: 0:00:21 lr: 0.000000 loss: 35.9192 (53.3110) loss_classifier: 0.5948 (1.1048) loss_box_reg: 0.8445 (2.2483) loss_objectness: 23.4596 (33.2773) loss_rpn_box_reg: 8.9517 (16.6807) time: 0.8901 data: 0.0232 max mem: 6508 Epoch: [14] [ 90/104] eta: 0:00:12 lr: 0.000000 loss: 33.5233 (53.9477) loss_classifier: 0.6733 (1.0888) loss_box_reg: 0.9395 (2.1349) loss_objectness: 20.6789 (33.4692) loss_rpn_box_reg: 9.4732 (17.2548) time: 0.8903 data: 0.0207 max mem: 6508 Epoch: [14] [100/104] eta: 0:00:03 lr: 0.000000 loss: 41.9988 (54.3623) loss_classifier: 0.7239 (1.1340) loss_box_reg: 0.9588 (2.1179) loss_objectness: 27.4681 (33.7159) loss_rpn_box_reg: 9.7129 (17.3945) time: 0.9045 data: 0.0208 max mem: 6508 Epoch: [14] [103/104] eta: 0:00:00 lr: 0.000000 loss: 41.9988 (53.9387) loss_classifier: 0.6733 (1.1094) loss_box_reg: 0.8385 (2.0599) loss_objectness: 27.8470 (33.3843) loss_rpn_box_reg: 11.5158 (17.3851) time: 0.9092 data: 0.0206 max mem: 6508 Epoch: [14] Total time: 0:01:34 (0.9127 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.4477 (0.4477) evaluator_time: 0.0099 (0.0099) time: 1.2394 data: 0.7516 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.3715 (0.3737) evaluator_time: 0.0051 (0.0055) time: 0.4060 data: 0.0216 max mem: 6508 Test: Total time: 0:00:11 (0.4405 s / it) Averaged stats: model_time: 0.3715 (0.3737) evaluator_time: 0.0051 (0.0055) Accumulating evaluation results... DONE (t=0.06s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
#save rmsprob
import pickle
Filename = "FRCNN3rmsprob.pkl"
# Define the file path where you want to save the model
filename = "/content/drive/MyDrive/dataset1/FRCNN3rmsprob.pkl"
# Save the model to the specified file path
torch.save(model.state_dict(), filename)
# Save the Modle to file in the current working directory
with open(Filename, 'wb') as file:
pickle.dump(model, file)
# Load the Model back from file
with open(Filename, 'rb') as file:
model = pickle.load(file)
model
FasterRCNN(
(transform): GeneralizedRCNNTransform(
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
Resize(min_size=(800,), max_size=1333, mode='bilinear')
)
(backbone): BackboneWithFPN(
(body): IntermediateLayerGetter(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): FrozenBatchNorm2d(256, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(512, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(1024, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(2048, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
)
)
(fpn): FeaturePyramidNetwork(
(inner_blocks): ModuleList(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
)
(1): Conv2dNormActivation(
(0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
(2): Conv2dNormActivation(
(0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
)
(3): Conv2dNormActivation(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
)
)
(layer_blocks): ModuleList(
(0-3): 4 x Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(extra_blocks): LastLevelMaxPool()
)
)
(rpn): RegionProposalNetwork(
(anchor_generator): AnchorGenerator()
(head): RPNHead(
(conv): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
)
)
(cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
(bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
)
)
(roi_heads): RoIHeads(
(box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)
(box_head): TwoMLPHead(
(fc6): Linear(in_features=12544, out_features=1024, bias=True)
(fc7): Linear(in_features=1024, out_features=1024, bias=True)
)
(box_predictor): FastRCNNPredictor(
(cls_score): Linear(in_features=1024, out_features=11, bias=True)
(bbox_pred): Linear(in_features=1024, out_features=44, bias=True)
)
)
)
#adelta
# to train on GPU if selected
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# number of classes
num_classes = 11
# get the model using our helper function
model = get_object_detection_model(num_classes)
# move model to the right device
model.to(device)
# construct an optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.Adadelta(params, lr=0.001, rho=0.9, eps=1e-06, weight_decay=0.0005)
# and a learning rate scheduler which decreases the learning rate by
# 10x every 3 epochs
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=3,
gamma=0.1)
# training for 8 epochs # adekta
num_epochs = 15
for epoch in range(num_epochs):
# training for one epoch
train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
# update the learning rate
lr_scheduler.step()
# evaluate on the test dataset
evaluate(model, data_loader_test, device=device)
Epoch: [0] [ 0/104] eta: 0:03:49 lr: 0.000011 loss: 4.0759 (4.0759) loss_classifier: 2.5687 (2.5687) loss_box_reg: 0.2849 (0.2849) loss_objectness: 1.1855 (1.1855) loss_rpn_box_reg: 0.0368 (0.0368) time: 2.2106 data: 0.9448 max mem: 6508 Epoch: [0] [ 10/104] eta: 0:02:00 lr: 0.000108 loss: 3.3600 (3.3749) loss_classifier: 2.6055 (2.6007) loss_box_reg: 0.2849 (0.2693) loss_objectness: 0.3081 (0.4810) loss_rpn_box_reg: 0.0216 (0.0239) time: 1.2857 data: 0.1197 max mem: 6508 Epoch: [0] [ 20/104] eta: 0:01:43 lr: 0.000205 loss: 3.1378 (3.2732) loss_classifier: 2.5849 (2.5738) loss_box_reg: 0.1922 (0.2402) loss_objectness: 0.2341 (0.4338) loss_rpn_box_reg: 0.0178 (0.0253) time: 1.1830 data: 0.0391 max mem: 6508 Epoch: [0] [ 30/104] eta: 0:01:28 lr: 0.000302 loss: 2.9602 (3.1843) loss_classifier: 2.5095 (2.5413) loss_box_reg: 0.1580 (0.2168) loss_objectness: 0.2463 (0.4034) loss_rpn_box_reg: 0.0178 (0.0228) time: 1.1476 data: 0.0378 max mem: 6508 Epoch: [0] [ 40/104] eta: 0:01:14 lr: 0.000399 loss: 2.9602 (3.1469) loss_classifier: 2.4297 (2.4981) loss_box_reg: 0.1765 (0.2324) loss_objectness: 0.3229 (0.3927) loss_rpn_box_reg: 0.0201 (0.0237) time: 1.0834 data: 0.0269 max mem: 6508 Epoch: [0] [ 50/104] eta: 0:01:01 lr: 0.000496 loss: 2.8197 (3.0742) loss_classifier: 2.2737 (2.4437) loss_box_reg: 0.2803 (0.2405) loss_objectness: 0.3229 (0.3664) loss_rpn_box_reg: 0.0216 (0.0236) time: 1.0403 data: 0.0201 max mem: 6508 Epoch: [0] [ 60/104] eta: 0:00:49 lr: 0.000593 loss: 2.6611 (2.9950) loss_classifier: 2.1325 (2.3786) loss_box_reg: 0.2608 (0.2478) loss_objectness: 0.2339 (0.3454) loss_rpn_box_reg: 0.0205 (0.0232) time: 1.0377 data: 0.0210 max mem: 6508 Epoch: [0] [ 70/104] eta: 0:00:37 lr: 0.000690 loss: 2.4501 (2.8952) loss_classifier: 1.9114 (2.3037) loss_box_reg: 0.2562 (0.2480) loss_objectness: 0.1492 (0.3212) loss_rpn_box_reg: 0.0181 (0.0222) time: 1.0399 data: 0.0207 max mem: 6508 Epoch: [0] [ 80/104] eta: 0:00:26 lr: 0.000787 loss: 2.0663 (2.7903) loss_classifier: 1.7363 (2.2144) loss_box_reg: 0.2166 (0.2499) loss_objectness: 0.1413 (0.3041) loss_rpn_box_reg: 0.0146 (0.0218) time: 1.0462 data: 0.0195 max mem: 6508 Epoch: [0] [ 90/104] eta: 0:00:15 lr: 0.000884 loss: 1.8681 (2.6737) loss_classifier: 1.3998 (2.1079) loss_box_reg: 0.2123 (0.2480) loss_objectness: 0.1271 (0.2961) loss_rpn_box_reg: 0.0156 (0.0217) time: 1.0609 data: 0.0198 max mem: 6508 Epoch: [0] [100/104] eta: 0:00:04 lr: 0.000981 loss: 1.6345 (2.5573) loss_classifier: 1.1006 (1.9976) loss_box_reg: 0.2507 (0.2551) loss_objectness: 0.1404 (0.2830) loss_rpn_box_reg: 0.0193 (0.0217) time: 1.0691 data: 0.0198 max mem: 6508 Epoch: [0] [103/104] eta: 0:00:01 lr: 0.001000 loss: 1.6345 (2.5272) loss_classifier: 1.0552 (1.9649) loss_box_reg: 0.2609 (0.2581) loss_objectness: 0.1482 (0.2819) loss_rpn_box_reg: 0.0211 (0.0224) time: 1.0684 data: 0.0193 max mem: 6508 Epoch: [0] Total time: 0:01:53 (1.0955 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.5306 (0.5306) evaluator_time: 0.0470 (0.0470) time: 1.2572 data: 0.6638 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4439 (0.4468) evaluator_time: 0.0204 (0.0226) time: 0.4938 data: 0.0198 max mem: 6508 Test: Total time: 0:00:13 (0.5274 s / it) Averaged stats: model_time: 0.4439 (0.4468) evaluator_time: 0.0204 (0.0226) Accumulating evaluation results... DONE (t=0.34s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.011 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.016 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.014 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.006 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008 Epoch: [1] [ 0/104] eta: 0:06:03 lr: 0.001000 loss: 1.1351 (1.1351) loss_classifier: 0.7006 (0.7006) loss_box_reg: 0.2811 (0.2811) loss_objectness: 0.1311 (0.1311) loss_rpn_box_reg: 0.0222 (0.0222) time: 3.4963 data: 1.7947 max mem: 6508 Epoch: [1] [ 10/104] eta: 0:02:07 lr: 0.001000 loss: 1.1702 (1.2361) loss_classifier: 0.7006 (0.7172) loss_box_reg: 0.3579 (0.3686) loss_objectness: 0.0991 (0.1220) loss_rpn_box_reg: 0.0222 (0.0283) time: 1.3578 data: 0.1842 max mem: 6508 Epoch: [1] [ 20/104] eta: 0:01:44 lr: 0.001000 loss: 1.0701 (1.1490) loss_classifier: 0.5925 (0.6332) loss_box_reg: 0.3021 (0.3316) loss_objectness: 0.0991 (0.1561) loss_rpn_box_reg: 0.0203 (0.0282) time: 1.1287 data: 0.0231 max mem: 6508 Epoch: [1] [ 30/104] eta: 0:01:28 lr: 0.001000 loss: 0.9767 (1.1083) loss_classifier: 0.5344 (0.6057) loss_box_reg: 0.3021 (0.3298) loss_objectness: 0.1022 (0.1467) loss_rpn_box_reg: 0.0179 (0.0262) time: 1.1007 data: 0.0215 max mem: 6508 Epoch: [1] [ 40/104] eta: 0:01:14 lr: 0.001000 loss: 0.9272 (1.0977) loss_classifier: 0.5014 (0.5869) loss_box_reg: 0.2982 (0.3314) loss_objectness: 0.1022 (0.1527) loss_rpn_box_reg: 0.0179 (0.0267) time: 1.0782 data: 0.0200 max mem: 6508 Epoch: [1] [ 50/104] eta: 0:01:01 lr: 0.001000 loss: 0.8314 (1.0514) loss_classifier: 0.4376 (0.5612) loss_box_reg: 0.2701 (0.3235) loss_objectness: 0.1048 (0.1416) loss_rpn_box_reg: 0.0159 (0.0251) time: 1.0561 data: 0.0195 max mem: 6508 Epoch: [1] [ 60/104] eta: 0:00:49 lr: 0.001000 loss: 0.8384 (1.0255) loss_classifier: 0.4454 (0.5453) loss_box_reg: 0.2669 (0.3180) loss_objectness: 0.1022 (0.1381) loss_rpn_box_reg: 0.0159 (0.0241) time: 1.0470 data: 0.0209 max mem: 6508 Epoch: [1] [ 70/104] eta: 0:00:37 lr: 0.001000 loss: 0.9086 (1.0151) loss_classifier: 0.4537 (0.5380) loss_box_reg: 0.2953 (0.3217) loss_objectness: 0.0950 (0.1325) loss_rpn_box_reg: 0.0155 (0.0230) time: 1.0571 data: 0.0259 max mem: 6508 Epoch: [1] [ 80/104] eta: 0:00:26 lr: 0.001000 loss: 0.8851 (0.9949) loss_classifier: 0.4499 (0.5273) loss_box_reg: 0.2936 (0.3193) loss_objectness: 0.0816 (0.1264) loss_rpn_box_reg: 0.0130 (0.0219) time: 1.0643 data: 0.0281 max mem: 6508 Epoch: [1] [ 90/104] eta: 0:00:15 lr: 0.001000 loss: 0.8026 (0.9790) loss_classifier: 0.4275 (0.5167) loss_box_reg: 0.2936 (0.3164) loss_objectness: 0.0677 (0.1242) loss_rpn_box_reg: 0.0123 (0.0218) time: 1.0691 data: 0.0244 max mem: 6508 Epoch: [1] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.9113 (0.9848) loss_classifier: 0.4669 (0.5179) loss_box_reg: 0.3101 (0.3238) loss_objectness: 0.0627 (0.1213) loss_rpn_box_reg: 0.0149 (0.0218) time: 1.0735 data: 0.0212 max mem: 6508 Epoch: [1] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.9113 (0.9854) loss_classifier: 0.4463 (0.5175) loss_box_reg: 0.3214 (0.3253) loss_objectness: 0.0627 (0.1205) loss_rpn_box_reg: 0.0173 (0.0220) time: 1.0693 data: 0.0209 max mem: 6508 Epoch: [1] Total time: 0:01:54 (1.1039 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:46 model_time: 0.6684 (0.6684) evaluator_time: 0.0484 (0.0484) time: 1.8000 data: 1.0774 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4410 (0.4497) evaluator_time: 0.0165 (0.0178) time: 0.4830 data: 0.0187 max mem: 6508 Test: Total time: 0:00:14 (0.5398 s / it) Averaged stats: model_time: 0.4410 (0.4497) evaluator_time: 0.0165 (0.0178) Accumulating evaluation results... DONE (t=0.26s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.011 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.030 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.005 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.018 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.006 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.006 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.041 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.051 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.107 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.038 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [2] [ 0/104] eta: 0:04:23 lr: 0.001000 loss: 0.9163 (0.9163) loss_classifier: 0.4803 (0.4803) loss_box_reg: 0.3495 (0.3495) loss_objectness: 0.0612 (0.0612) loss_rpn_box_reg: 0.0254 (0.0254) time: 2.5327 data: 1.2497 max mem: 6508 Epoch: [2] [ 10/104] eta: 0:01:54 lr: 0.001000 loss: 0.9163 (0.9454) loss_classifier: 0.4786 (0.4850) loss_box_reg: 0.3385 (0.3521) loss_objectness: 0.0614 (0.0885) loss_rpn_box_reg: 0.0140 (0.0198) time: 1.2232 data: 0.1286 max mem: 6508 Epoch: [2] [ 20/104] eta: 0:01:38 lr: 0.001000 loss: 0.9086 (0.9703) loss_classifier: 0.4786 (0.4987) loss_box_reg: 0.3239 (0.3639) loss_objectness: 0.0696 (0.0875) loss_rpn_box_reg: 0.0162 (0.0201) time: 1.1009 data: 0.0191 max mem: 6508 Epoch: [2] [ 30/104] eta: 0:01:24 lr: 0.001000 loss: 0.9709 (0.9801) loss_classifier: 0.5130 (0.5027) loss_box_reg: 0.3860 (0.3734) loss_objectness: 0.0691 (0.0841) loss_rpn_box_reg: 0.0168 (0.0199) time: 1.1000 data: 0.0229 max mem: 6508 Epoch: [2] [ 40/104] eta: 0:01:12 lr: 0.001000 loss: 0.9518 (0.9552) loss_classifier: 0.5024 (0.4898) loss_box_reg: 0.3826 (0.3658) loss_objectness: 0.0632 (0.0802) loss_rpn_box_reg: 0.0146 (0.0194) time: 1.0862 data: 0.0257 max mem: 6508 Epoch: [2] [ 50/104] eta: 0:01:00 lr: 0.001000 loss: 0.8419 (0.9578) loss_classifier: 0.4381 (0.4930) loss_box_reg: 0.3406 (0.3668) loss_objectness: 0.0576 (0.0781) loss_rpn_box_reg: 0.0143 (0.0199) time: 1.0854 data: 0.0258 max mem: 6508 Epoch: [2] [ 60/104] eta: 0:00:48 lr: 0.001000 loss: 0.8895 (0.9859) loss_classifier: 0.4577 (0.5066) loss_box_reg: 0.3665 (0.3822) loss_objectness: 0.0575 (0.0766) loss_rpn_box_reg: 0.0192 (0.0205) time: 1.0645 data: 0.0218 max mem: 6508 Epoch: [2] [ 70/104] eta: 0:00:37 lr: 0.001000 loss: 0.9628 (0.9828) loss_classifier: 0.5025 (0.5034) loss_box_reg: 0.3798 (0.3791) loss_objectness: 0.0557 (0.0795) loss_rpn_box_reg: 0.0189 (0.0208) time: 1.0458 data: 0.0200 max mem: 6508 Epoch: [2] [ 80/104] eta: 0:00:26 lr: 0.001000 loss: 0.9628 (0.9918) loss_classifier: 0.5025 (0.5083) loss_box_reg: 0.3798 (0.3861) loss_objectness: 0.0529 (0.0767) loss_rpn_box_reg: 0.0128 (0.0207) time: 1.0572 data: 0.0214 max mem: 6508 Epoch: [2] [ 90/104] eta: 0:00:15 lr: 0.001000 loss: 0.8832 (0.9863) loss_classifier: 0.4909 (0.5062) loss_box_reg: 0.3771 (0.3855) loss_objectness: 0.0475 (0.0745) loss_rpn_box_reg: 0.0130 (0.0202) time: 1.0670 data: 0.0217 max mem: 6508 Epoch: [2] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.8006 (0.9714) loss_classifier: 0.4076 (0.4977) loss_box_reg: 0.3359 (0.3820) loss_objectness: 0.0467 (0.0722) loss_rpn_box_reg: 0.0130 (0.0196) time: 1.0693 data: 0.0195 max mem: 6508 Epoch: [2] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.8006 (0.9672) loss_classifier: 0.4076 (0.4962) loss_box_reg: 0.3424 (0.3807) loss_objectness: 0.0454 (0.0710) loss_rpn_box_reg: 0.0102 (0.0193) time: 1.0710 data: 0.0193 max mem: 6508 Epoch: [2] Total time: 0:01:53 (1.0908 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:33 model_time: 0.5147 (0.5147) evaluator_time: 0.0170 (0.0170) time: 1.2991 data: 0.7548 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4410 (0.4421) evaluator_time: 0.0223 (0.0248) time: 0.4912 data: 0.0194 max mem: 6508 Test: Total time: 0:00:13 (0.5259 s / it) Averaged stats: model_time: 0.4410 (0.4421) evaluator_time: 0.0223 (0.0248) Accumulating evaluation results... DONE (t=0.18s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.025 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.065 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.012 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.031 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.023 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.077 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.104 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.135 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.139 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025 Epoch: [3] [ 0/104] eta: 0:03:15 lr: 0.000100 loss: 1.0471 (1.0471) loss_classifier: 0.5305 (0.5305) loss_box_reg: 0.4053 (0.4053) loss_objectness: 0.0849 (0.0849) loss_rpn_box_reg: 0.0264 (0.0264) time: 1.8781 data: 0.7191 max mem: 6508 Epoch: [3] [ 10/104] eta: 0:01:50 lr: 0.000100 loss: 1.0512 (1.1071) loss_classifier: 0.5372 (0.5524) loss_box_reg: 0.4420 (0.4525) loss_objectness: 0.0744 (0.0766) loss_rpn_box_reg: 0.0263 (0.0257) time: 1.1776 data: 0.0861 max mem: 6508 Epoch: [3] [ 20/104] eta: 0:01:36 lr: 0.000100 loss: 1.0315 (1.0544) loss_classifier: 0.5223 (0.5318) loss_box_reg: 0.4315 (0.4377) loss_objectness: 0.0517 (0.0633) loss_rpn_box_reg: 0.0218 (0.0215) time: 1.1065 data: 0.0210 max mem: 6508 Epoch: [3] [ 30/104] eta: 0:01:23 lr: 0.000100 loss: 0.9557 (1.0177) loss_classifier: 0.4734 (0.5138) loss_box_reg: 0.3947 (0.4212) loss_objectness: 0.0451 (0.0626) loss_rpn_box_reg: 0.0136 (0.0201) time: 1.0928 data: 0.0195 max mem: 6508 Epoch: [3] [ 40/104] eta: 0:01:11 lr: 0.000100 loss: 0.9115 (0.9881) loss_classifier: 0.4660 (0.5027) loss_box_reg: 0.3792 (0.4083) loss_objectness: 0.0462 (0.0583) loss_rpn_box_reg: 0.0135 (0.0188) time: 1.0741 data: 0.0208 max mem: 6508 Epoch: [3] [ 50/104] eta: 0:00:59 lr: 0.000100 loss: 0.9662 (0.9973) loss_classifier: 0.5089 (0.5078) loss_box_reg: 0.4089 (0.4124) loss_objectness: 0.0467 (0.0587) loss_rpn_box_reg: 0.0165 (0.0184) time: 1.0588 data: 0.0212 max mem: 6508 Epoch: [3] [ 60/104] eta: 0:00:47 lr: 0.000100 loss: 0.9121 (0.9782) loss_classifier: 0.4642 (0.4988) loss_box_reg: 0.3875 (0.4065) loss_objectness: 0.0473 (0.0553) loss_rpn_box_reg: 0.0128 (0.0175) time: 1.0466 data: 0.0205 max mem: 6508 Epoch: [3] [ 70/104] eta: 0:00:36 lr: 0.000100 loss: 0.8367 (0.9782) loss_classifier: 0.4401 (0.4975) loss_box_reg: 0.3690 (0.4061) loss_objectness: 0.0472 (0.0564) loss_rpn_box_reg: 0.0111 (0.0182) time: 1.0455 data: 0.0202 max mem: 6508 Epoch: [3] [ 80/104] eta: 0:00:25 lr: 0.000100 loss: 1.0318 (0.9807) loss_classifier: 0.5163 (0.4972) loss_box_reg: 0.3980 (0.4057) loss_objectness: 0.0539 (0.0591) loss_rpn_box_reg: 0.0163 (0.0187) time: 1.0587 data: 0.0215 max mem: 6508 Epoch: [3] [ 90/104] eta: 0:00:15 lr: 0.000100 loss: 0.9981 (0.9717) loss_classifier: 0.4935 (0.4919) loss_box_reg: 0.3643 (0.4011) loss_objectness: 0.0574 (0.0605) loss_rpn_box_reg: 0.0160 (0.0181) time: 1.0741 data: 0.0221 max mem: 6508 Epoch: [3] [100/104] eta: 0:00:04 lr: 0.000100 loss: 0.9981 (0.9730) loss_classifier: 0.5089 (0.4929) loss_box_reg: 0.4189 (0.4013) loss_objectness: 0.0501 (0.0604) loss_rpn_box_reg: 0.0158 (0.0183) time: 1.0747 data: 0.0209 max mem: 6508 Epoch: [3] [103/104] eta: 0:00:01 lr: 0.000100 loss: 1.0118 (0.9736) loss_classifier: 0.5108 (0.4932) loss_box_reg: 0.4203 (0.4021) loss_objectness: 0.0478 (0.0599) loss_rpn_box_reg: 0.0167 (0.0183) time: 1.0769 data: 0.0216 max mem: 6508 Epoch: [3] Total time: 0:01:52 (1.0812 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:31 model_time: 0.5568 (0.5568) evaluator_time: 0.0257 (0.0257) time: 1.2150 data: 0.6123 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4423 (0.4446) evaluator_time: 0.0232 (0.0265) time: 0.4953 data: 0.0208 max mem: 6508 Test: Total time: 0:00:13 (0.5257 s / it) Averaged stats: model_time: 0.4423 (0.4446) evaluator_time: 0.0232 (0.0265) Accumulating evaluation results... DONE (t=0.18s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.026 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.069 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.012 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.032 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.025 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.082 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.114 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.152 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025 Epoch: [4] [ 0/104] eta: 0:03:14 lr: 0.000100 loss: 1.0600 (1.0600) loss_classifier: 0.5243 (0.5243) loss_box_reg: 0.4688 (0.4688) loss_objectness: 0.0506 (0.0506) loss_rpn_box_reg: 0.0161 (0.0161) time: 1.8730 data: 0.6827 max mem: 6508 Epoch: [4] [ 10/104] eta: 0:01:49 lr: 0.000100 loss: 0.9999 (1.0436) loss_classifier: 0.5243 (0.5329) loss_box_reg: 0.4057 (0.4451) loss_objectness: 0.0481 (0.0462) loss_rpn_box_reg: 0.0161 (0.0193) time: 1.1693 data: 0.0823 max mem: 6508 Epoch: [4] [ 20/104] eta: 0:01:35 lr: 0.000100 loss: 0.9513 (1.0134) loss_classifier: 0.4843 (0.5122) loss_box_reg: 0.3950 (0.4227) loss_objectness: 0.0481 (0.0597) loss_rpn_box_reg: 0.0160 (0.0187) time: 1.1053 data: 0.0205 max mem: 6508 Epoch: [4] [ 30/104] eta: 0:01:23 lr: 0.000100 loss: 0.9513 (0.9962) loss_classifier: 0.4843 (0.5028) loss_box_reg: 0.3740 (0.4104) loss_objectness: 0.0565 (0.0631) loss_rpn_box_reg: 0.0195 (0.0198) time: 1.1017 data: 0.0205 max mem: 6508 Epoch: [4] [ 40/104] eta: 0:01:11 lr: 0.000100 loss: 0.9775 (0.9923) loss_classifier: 0.4883 (0.5035) loss_box_reg: 0.4030 (0.4087) loss_objectness: 0.0565 (0.0606) loss_rpn_box_reg: 0.0212 (0.0195) time: 1.0805 data: 0.0224 max mem: 6508 Epoch: [4] [ 50/104] eta: 0:00:59 lr: 0.000100 loss: 0.8397 (0.9709) loss_classifier: 0.4366 (0.4946) loss_box_reg: 0.3437 (0.4013) loss_objectness: 0.0421 (0.0570) loss_rpn_box_reg: 0.0129 (0.0181) time: 1.0649 data: 0.0245 max mem: 6508 Epoch: [4] [ 60/104] eta: 0:00:48 lr: 0.000100 loss: 0.8397 (0.9653) loss_classifier: 0.4268 (0.4909) loss_box_reg: 0.3625 (0.4005) loss_objectness: 0.0399 (0.0568) loss_rpn_box_reg: 0.0093 (0.0171) time: 1.0545 data: 0.0240 max mem: 6508 Epoch: [4] [ 70/104] eta: 0:00:36 lr: 0.000100 loss: 0.8745 (0.9758) loss_classifier: 0.4384 (0.4945) loss_box_reg: 0.3625 (0.4033) loss_objectness: 0.0562 (0.0594) loss_rpn_box_reg: 0.0174 (0.0186) time: 1.0504 data: 0.0209 max mem: 6508 Epoch: [4] [ 80/104] eta: 0:00:26 lr: 0.000100 loss: 0.8969 (0.9721) loss_classifier: 0.4830 (0.4912) loss_box_reg: 0.3493 (0.4027) loss_objectness: 0.0634 (0.0593) loss_rpn_box_reg: 0.0193 (0.0190) time: 1.0562 data: 0.0203 max mem: 6508 Epoch: [4] [ 90/104] eta: 0:00:15 lr: 0.000100 loss: 0.8969 (0.9712) loss_classifier: 0.4673 (0.4908) loss_box_reg: 0.3838 (0.4038) loss_objectness: 0.0489 (0.0584) loss_rpn_box_reg: 0.0137 (0.0181) time: 1.0652 data: 0.0198 max mem: 6508 Epoch: [4] [100/104] eta: 0:00:04 lr: 0.000100 loss: 0.9435 (0.9748) loss_classifier: 0.4673 (0.4935) loss_box_reg: 0.3838 (0.4057) loss_objectness: 0.0452 (0.0574) loss_rpn_box_reg: 0.0137 (0.0182) time: 1.0699 data: 0.0194 max mem: 6508 Epoch: [4] [103/104] eta: 0:00:01 lr: 0.000100 loss: 0.9443 (0.9707) loss_classifier: 0.4772 (0.4914) loss_box_reg: 0.3838 (0.4038) loss_objectness: 0.0440 (0.0574) loss_rpn_box_reg: 0.0137 (0.0181) time: 1.0685 data: 0.0189 max mem: 6508 Epoch: [4] Total time: 0:01:52 (1.0821 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:33 model_time: 0.5721 (0.5721) evaluator_time: 0.0310 (0.0310) time: 1.2730 data: 0.6565 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4422 (0.4454) evaluator_time: 0.0264 (0.0278) time: 0.4995 data: 0.0224 max mem: 6508 Test: Total time: 0:00:13 (0.5306 s / it) Averaged stats: model_time: 0.4422 (0.4454) evaluator_time: 0.0264 (0.0278) Accumulating evaluation results... DONE (t=0.20s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.027 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.071 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.013 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.035 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.027 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.084 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.119 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.140 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.151 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025 Epoch: [5] [ 0/104] eta: 0:03:18 lr: 0.000100 loss: 0.8089 (0.8089) loss_classifier: 0.4197 (0.4197) loss_box_reg: 0.3246 (0.3246) loss_objectness: 0.0520 (0.0520) loss_rpn_box_reg: 0.0127 (0.0127) time: 1.9080 data: 0.7834 max mem: 6508 Epoch: [5] [ 10/104] eta: 0:01:50 lr: 0.000100 loss: 0.8925 (0.8742) loss_classifier: 0.4646 (0.4502) loss_box_reg: 0.3785 (0.3702) loss_objectness: 0.0391 (0.0418) loss_rpn_box_reg: 0.0127 (0.0121) time: 1.1762 data: 0.0891 max mem: 6508 Epoch: [5] [ 20/104] eta: 0:01:36 lr: 0.000100 loss: 0.8562 (0.8700) loss_classifier: 0.4325 (0.4480) loss_box_reg: 0.3719 (0.3631) loss_objectness: 0.0382 (0.0445) loss_rpn_box_reg: 0.0129 (0.0144) time: 1.1103 data: 0.0208 max mem: 6508 Epoch: [5] [ 30/104] eta: 0:01:23 lr: 0.000100 loss: 0.9047 (0.9220) loss_classifier: 0.4699 (0.4718) loss_box_reg: 0.3729 (0.3816) loss_objectness: 0.0393 (0.0513) loss_rpn_box_reg: 0.0143 (0.0173) time: 1.1017 data: 0.0206 max mem: 6508 Epoch: [5] [ 40/104] eta: 0:01:11 lr: 0.000100 loss: 0.9314 (0.9486) loss_classifier: 0.4938 (0.4789) loss_box_reg: 0.3978 (0.3955) loss_objectness: 0.0637 (0.0568) loss_rpn_box_reg: 0.0147 (0.0175) time: 1.0731 data: 0.0194 max mem: 6508 Epoch: [5] [ 50/104] eta: 0:00:59 lr: 0.000100 loss: 0.8906 (0.9427) loss_classifier: 0.4345 (0.4750) loss_box_reg: 0.3787 (0.3938) loss_objectness: 0.0479 (0.0572) loss_rpn_box_reg: 0.0133 (0.0167) time: 1.0553 data: 0.0204 max mem: 6508 Epoch: [5] [ 60/104] eta: 0:00:48 lr: 0.000100 loss: 0.9511 (0.9705) loss_classifier: 0.4656 (0.4899) loss_box_reg: 0.3845 (0.4073) loss_objectness: 0.0470 (0.0564) loss_rpn_box_reg: 0.0150 (0.0169) time: 1.0508 data: 0.0218 max mem: 6508 Epoch: [5] [ 70/104] eta: 0:00:36 lr: 0.000100 loss: 1.0254 (0.9822) loss_classifier: 0.5239 (0.4963) loss_box_reg: 0.4201 (0.4107) loss_objectness: 0.0485 (0.0576) loss_rpn_box_reg: 0.0179 (0.0175) time: 1.0508 data: 0.0211 max mem: 6508 Epoch: [5] [ 80/104] eta: 0:00:25 lr: 0.000100 loss: 1.0060 (0.9836) loss_classifier: 0.5202 (0.4971) loss_box_reg: 0.4178 (0.4106) loss_objectness: 0.0518 (0.0574) loss_rpn_box_reg: 0.0204 (0.0185) time: 1.0523 data: 0.0197 max mem: 6508 Epoch: [5] [ 90/104] eta: 0:00:15 lr: 0.000100 loss: 1.0086 (0.9848) loss_classifier: 0.4859 (0.4972) loss_box_reg: 0.4331 (0.4118) loss_objectness: 0.0493 (0.0575) loss_rpn_box_reg: 0.0192 (0.0184) time: 1.0629 data: 0.0209 max mem: 6508 Epoch: [5] [100/104] eta: 0:00:04 lr: 0.000100 loss: 0.9006 (0.9680) loss_classifier: 0.4366 (0.4888) loss_box_reg: 0.3988 (0.4045) loss_objectness: 0.0493 (0.0567) loss_rpn_box_reg: 0.0140 (0.0180) time: 1.0718 data: 0.0206 max mem: 6508 Epoch: [5] [103/104] eta: 0:00:01 lr: 0.000100 loss: 0.9371 (0.9711) loss_classifier: 0.4366 (0.4910) loss_box_reg: 0.4087 (0.4058) loss_objectness: 0.0494 (0.0563) loss_rpn_box_reg: 0.0170 (0.0180) time: 1.0674 data: 0.0187 max mem: 6508 Epoch: [5] Total time: 0:01:52 (1.0808 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:33 model_time: 0.5164 (0.5164) evaluator_time: 0.0355 (0.0355) time: 1.3025 data: 0.7321 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4429 (0.4461) evaluator_time: 0.0272 (0.0547) time: 0.5069 data: 0.0215 max mem: 6508 Test: Total time: 0:00:14 (0.5703 s / it) Averaged stats: model_time: 0.4429 (0.4461) evaluator_time: 0.0272 (0.0547) Accumulating evaluation results... DONE (t=0.20s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.028 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.014 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.036 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.030 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.085 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.122 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.154 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.154 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025 Epoch: [6] [ 0/104] eta: 0:03:22 lr: 0.000010 loss: 1.2885 (1.2885) loss_classifier: 0.6427 (0.6427) loss_box_reg: 0.5884 (0.5884) loss_objectness: 0.0387 (0.0387) loss_rpn_box_reg: 0.0187 (0.0187) time: 1.9494 data: 0.8234 max mem: 6508 Epoch: [6] [ 10/104] eta: 0:01:51 lr: 0.000010 loss: 1.0491 (1.0071) loss_classifier: 0.5128 (0.5007) loss_box_reg: 0.4600 (0.4501) loss_objectness: 0.0419 (0.0397) loss_rpn_box_reg: 0.0158 (0.0166) time: 1.1824 data: 0.0937 max mem: 6508 Epoch: [6] [ 20/104] eta: 0:01:36 lr: 0.000010 loss: 0.9693 (0.9666) loss_classifier: 0.4798 (0.4856) loss_box_reg: 0.4007 (0.4134) loss_objectness: 0.0451 (0.0497) loss_rpn_box_reg: 0.0154 (0.0179) time: 1.1121 data: 0.0219 max mem: 6508 Epoch: [6] [ 30/104] eta: 0:01:23 lr: 0.000010 loss: 0.9755 (0.9744) loss_classifier: 0.4994 (0.4888) loss_box_reg: 0.3828 (0.4143) loss_objectness: 0.0528 (0.0530) loss_rpn_box_reg: 0.0178 (0.0182) time: 1.0983 data: 0.0216 max mem: 6508 Epoch: [6] [ 40/104] eta: 0:01:11 lr: 0.000010 loss: 0.9755 (0.9731) loss_classifier: 0.4994 (0.4879) loss_box_reg: 0.4114 (0.4137) loss_objectness: 0.0538 (0.0540) loss_rpn_box_reg: 0.0154 (0.0176) time: 1.0684 data: 0.0194 max mem: 6508 Epoch: [6] [ 50/104] eta: 0:00:59 lr: 0.000010 loss: 0.9509 (0.9780) loss_classifier: 0.4764 (0.4903) loss_box_reg: 0.4237 (0.4153) loss_objectness: 0.0538 (0.0546) loss_rpn_box_reg: 0.0160 (0.0177) time: 1.0579 data: 0.0201 max mem: 6508 Epoch: [6] [ 60/104] eta: 0:00:48 lr: 0.000010 loss: 1.0108 (0.9757) loss_classifier: 0.5083 (0.4906) loss_box_reg: 0.4370 (0.4141) loss_objectness: 0.0433 (0.0535) loss_rpn_box_reg: 0.0160 (0.0175) time: 1.0574 data: 0.0230 max mem: 6508 Epoch: [6] [ 70/104] eta: 0:00:37 lr: 0.000010 loss: 0.8464 (0.9646) loss_classifier: 0.4210 (0.4869) loss_box_reg: 0.3655 (0.4074) loss_objectness: 0.0396 (0.0526) loss_rpn_box_reg: 0.0116 (0.0177) time: 1.0579 data: 0.0235 max mem: 6508 Epoch: [6] [ 80/104] eta: 0:00:26 lr: 0.000010 loss: 0.8103 (0.9520) loss_classifier: 0.4081 (0.4818) loss_box_reg: 0.3511 (0.4010) loss_objectness: 0.0350 (0.0518) loss_rpn_box_reg: 0.0116 (0.0174) time: 1.0585 data: 0.0215 max mem: 6508 Epoch: [6] [ 90/104] eta: 0:00:15 lr: 0.000010 loss: 0.9766 (0.9606) loss_classifier: 0.4935 (0.4864) loss_box_reg: 0.3786 (0.4059) loss_objectness: 0.0320 (0.0510) loss_rpn_box_reg: 0.0135 (0.0174) time: 1.0676 data: 0.0202 max mem: 6508 Epoch: [6] [100/104] eta: 0:00:04 lr: 0.000010 loss: 1.0320 (0.9658) loss_classifier: 0.4976 (0.4890) loss_box_reg: 0.4053 (0.4066) loss_objectness: 0.0450 (0.0526) loss_rpn_box_reg: 0.0163 (0.0176) time: 1.0749 data: 0.0204 max mem: 6508 Epoch: [6] [103/104] eta: 0:00:01 lr: 0.000010 loss: 1.0320 (0.9686) loss_classifier: 0.4948 (0.4894) loss_box_reg: 0.4036 (0.4065) loss_objectness: 0.0528 (0.0548) loss_rpn_box_reg: 0.0193 (0.0179) time: 1.0718 data: 0.0201 max mem: 6508 Epoch: [6] Total time: 0:01:52 (1.0839 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:31 model_time: 0.5650 (0.5650) evaluator_time: 0.0297 (0.0297) time: 1.2216 data: 0.6035 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4417 (0.4446) evaluator_time: 0.0195 (0.0298) time: 0.4984 data: 0.0210 max mem: 6508 Test: Total time: 0:00:13 (0.5318 s / it) Averaged stats: model_time: 0.4417 (0.4446) evaluator_time: 0.0195 (0.0298) Accumulating evaluation results... DONE (t=0.20s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.028 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.013 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.036 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.029 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.085 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.122 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.153 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025 Epoch: [7] [ 0/104] eta: 0:03:27 lr: 0.000010 loss: 1.4218 (1.4218) loss_classifier: 0.6696 (0.6696) loss_box_reg: 0.6648 (0.6648) loss_objectness: 0.0660 (0.0660) loss_rpn_box_reg: 0.0214 (0.0214) time: 1.9908 data: 0.8677 max mem: 6508 Epoch: [7] [ 10/104] eta: 0:01:50 lr: 0.000010 loss: 1.0095 (1.0092) loss_classifier: 0.4840 (0.5016) loss_box_reg: 0.4434 (0.4374) loss_objectness: 0.0545 (0.0541) loss_rpn_box_reg: 0.0150 (0.0161) time: 1.1727 data: 0.0946 max mem: 6508 Epoch: [7] [ 20/104] eta: 0:01:36 lr: 0.000010 loss: 1.0162 (1.0178) loss_classifier: 0.4840 (0.5060) loss_box_reg: 0.4568 (0.4420) loss_objectness: 0.0520 (0.0531) loss_rpn_box_reg: 0.0150 (0.0168) time: 1.1019 data: 0.0183 max mem: 6508 Epoch: [7] [ 30/104] eta: 0:01:23 lr: 0.000010 loss: 1.0405 (0.9977) loss_classifier: 0.4935 (0.5015) loss_box_reg: 0.3845 (0.4230) loss_objectness: 0.0520 (0.0559) loss_rpn_box_reg: 0.0181 (0.0173) time: 1.1004 data: 0.0198 max mem: 6508 Epoch: [7] [ 40/104] eta: 0:01:11 lr: 0.000010 loss: 0.8868 (0.9853) loss_classifier: 0.4536 (0.4965) loss_box_reg: 0.3749 (0.4200) loss_objectness: 0.0384 (0.0524) loss_rpn_box_reg: 0.0131 (0.0164) time: 1.0762 data: 0.0199 max mem: 6508 Epoch: [7] [ 50/104] eta: 0:00:59 lr: 0.000010 loss: 0.9059 (0.9954) loss_classifier: 0.4536 (0.5012) loss_box_reg: 0.3783 (0.4216) loss_objectness: 0.0384 (0.0548) loss_rpn_box_reg: 0.0150 (0.0178) time: 1.0583 data: 0.0196 max mem: 6508 Epoch: [7] [ 60/104] eta: 0:00:48 lr: 0.000010 loss: 0.9683 (0.9907) loss_classifier: 0.4741 (0.4998) loss_box_reg: 0.4048 (0.4182) loss_objectness: 0.0562 (0.0547) loss_rpn_box_reg: 0.0206 (0.0180) time: 1.0502 data: 0.0201 max mem: 6508 Epoch: [7] [ 70/104] eta: 0:00:36 lr: 0.000010 loss: 0.9683 (0.9950) loss_classifier: 0.4857 (0.5028) loss_box_reg: 0.4071 (0.4193) loss_objectness: 0.0417 (0.0547) loss_rpn_box_reg: 0.0165 (0.0182) time: 1.0503 data: 0.0217 max mem: 6508 Epoch: [7] [ 80/104] eta: 0:00:25 lr: 0.000010 loss: 0.9465 (0.9916) loss_classifier: 0.4834 (0.4989) loss_box_reg: 0.4071 (0.4179) loss_objectness: 0.0392 (0.0565) loss_rpn_box_reg: 0.0139 (0.0183) time: 1.0569 data: 0.0218 max mem: 6508 Epoch: [7] [ 90/104] eta: 0:00:15 lr: 0.000010 loss: 0.8826 (0.9701) loss_classifier: 0.4435 (0.4892) loss_box_reg: 0.3466 (0.4079) loss_objectness: 0.0408 (0.0554) loss_rpn_box_reg: 0.0126 (0.0176) time: 1.0605 data: 0.0203 max mem: 6508 Epoch: [7] [100/104] eta: 0:00:04 lr: 0.000010 loss: 0.8826 (0.9735) loss_classifier: 0.4553 (0.4914) loss_box_reg: 0.3637 (0.4088) loss_objectness: 0.0492 (0.0553) loss_rpn_box_reg: 0.0123 (0.0181) time: 1.0662 data: 0.0198 max mem: 6508 Epoch: [7] [103/104] eta: 0:00:01 lr: 0.000010 loss: 0.8776 (0.9691) loss_classifier: 0.4504 (0.4894) loss_box_reg: 0.3742 (0.4071) loss_objectness: 0.0539 (0.0548) loss_rpn_box_reg: 0.0123 (0.0179) time: 1.0656 data: 0.0190 max mem: 6508 Epoch: [7] Total time: 0:01:52 (1.0809 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:51 model_time: 0.6856 (0.6856) evaluator_time: 0.1232 (0.1232) time: 1.9657 data: 1.1203 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4401 (0.4496) evaluator_time: 0.0194 (0.0332) time: 0.4916 data: 0.0202 max mem: 6508 Test: Total time: 0:00:14 (0.5636 s / it) Averaged stats: model_time: 0.4401 (0.4496) evaluator_time: 0.0194 (0.0332) Accumulating evaluation results... DONE (t=0.34s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.029 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.014 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.036 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.030 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.124 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.156 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.154 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025 Epoch: [8] [ 0/104] eta: 0:04:54 lr: 0.000010 loss: 1.4561 (1.4561) loss_classifier: 0.6480 (0.6480) loss_box_reg: 0.5642 (0.5642) loss_objectness: 0.2178 (0.2178) loss_rpn_box_reg: 0.0261 (0.0261) time: 2.8293 data: 1.4711 max mem: 6508 Epoch: [8] [ 10/104] eta: 0:01:58 lr: 0.000010 loss: 1.0857 (1.0912) loss_classifier: 0.5417 (0.5415) loss_box_reg: 0.4551 (0.4548) loss_objectness: 0.0692 (0.0732) loss_rpn_box_reg: 0.0177 (0.0216) time: 1.2569 data: 0.1488 max mem: 6508 Epoch: [8] [ 20/104] eta: 0:01:40 lr: 0.000010 loss: 1.0857 (1.0959) loss_classifier: 0.5324 (0.5462) loss_box_reg: 0.4345 (0.4633) loss_objectness: 0.0358 (0.0657) loss_rpn_box_reg: 0.0145 (0.0207) time: 1.1151 data: 0.0215 max mem: 6508 Epoch: [8] [ 30/104] eta: 0:01:25 lr: 0.000010 loss: 0.9701 (1.0529) loss_classifier: 0.5126 (0.5277) loss_box_reg: 0.4303 (0.4479) loss_objectness: 0.0384 (0.0583) loss_rpn_box_reg: 0.0125 (0.0190) time: 1.1088 data: 0.0248 max mem: 6508 Epoch: [8] [ 40/104] eta: 0:01:12 lr: 0.000010 loss: 0.9484 (1.0251) loss_classifier: 0.4730 (0.5134) loss_box_reg: 0.4157 (0.4351) loss_objectness: 0.0396 (0.0582) loss_rpn_box_reg: 0.0114 (0.0184) time: 1.0783 data: 0.0226 max mem: 6508 Epoch: [8] [ 50/104] eta: 0:01:00 lr: 0.000010 loss: 0.8402 (0.9854) loss_classifier: 0.4363 (0.4962) loss_box_reg: 0.3572 (0.4167) loss_objectness: 0.0386 (0.0549) loss_rpn_box_reg: 0.0118 (0.0177) time: 1.0613 data: 0.0218 max mem: 6508 Epoch: [8] [ 60/104] eta: 0:00:48 lr: 0.000010 loss: 0.8402 (0.9767) loss_classifier: 0.4242 (0.4929) loss_box_reg: 0.3395 (0.4116) loss_objectness: 0.0426 (0.0539) loss_rpn_box_reg: 0.0169 (0.0182) time: 1.0469 data: 0.0207 max mem: 6508 Epoch: [8] [ 70/104] eta: 0:00:37 lr: 0.000010 loss: 0.9218 (0.9756) loss_classifier: 0.4895 (0.4916) loss_box_reg: 0.3836 (0.4118) loss_objectness: 0.0431 (0.0543) loss_rpn_box_reg: 0.0175 (0.0178) time: 1.0490 data: 0.0219 max mem: 6508 Epoch: [8] [ 80/104] eta: 0:00:26 lr: 0.000010 loss: 0.9491 (0.9673) loss_classifier: 0.4907 (0.4883) loss_box_reg: 0.3861 (0.4068) loss_objectness: 0.0524 (0.0548) loss_rpn_box_reg: 0.0136 (0.0175) time: 1.0606 data: 0.0241 max mem: 6508 Epoch: [8] [ 90/104] eta: 0:00:15 lr: 0.000010 loss: 0.9491 (0.9651) loss_classifier: 0.4843 (0.4879) loss_box_reg: 0.3844 (0.4062) loss_objectness: 0.0402 (0.0535) loss_rpn_box_reg: 0.0136 (0.0174) time: 1.0663 data: 0.0230 max mem: 6508 Epoch: [8] [100/104] eta: 0:00:04 lr: 0.000010 loss: 0.9549 (0.9688) loss_classifier: 0.4989 (0.4898) loss_box_reg: 0.3844 (0.4074) loss_objectness: 0.0385 (0.0541) loss_rpn_box_reg: 0.0160 (0.0176) time: 1.0727 data: 0.0209 max mem: 6508 Epoch: [8] [103/104] eta: 0:00:01 lr: 0.000010 loss: 0.9549 (0.9687) loss_classifier: 0.4989 (0.4893) loss_box_reg: 0.3844 (0.4069) loss_objectness: 0.0552 (0.0546) loss_rpn_box_reg: 0.0171 (0.0178) time: 1.0754 data: 0.0204 max mem: 6508 Epoch: [8] Total time: 0:01:53 (1.0933 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.5142 (0.5142) evaluator_time: 0.0289 (0.0289) time: 1.2555 data: 0.6940 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4425 (0.4429) evaluator_time: 0.0257 (0.0398) time: 0.4967 data: 0.0217 max mem: 6508 Test: Total time: 0:00:14 (0.5419 s / it) Averaged stats: model_time: 0.4425 (0.4429) evaluator_time: 0.0257 (0.0398) Accumulating evaluation results... DONE (t=0.20s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.029 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.077 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.014 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.037 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.030 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.014 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.125 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.157 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025 Epoch: [9] [ 0/104] eta: 0:03:31 lr: 0.000001 loss: 0.6304 (0.6304) loss_classifier: 0.3522 (0.3522) loss_box_reg: 0.2296 (0.2296) loss_objectness: 0.0376 (0.0376) loss_rpn_box_reg: 0.0110 (0.0110) time: 2.0295 data: 0.9336 max mem: 6508 Epoch: [9] [ 10/104] eta: 0:01:51 lr: 0.000001 loss: 0.9999 (1.0055) loss_classifier: 0.5041 (0.5084) loss_box_reg: 0.4280 (0.4258) loss_objectness: 0.0353 (0.0522) loss_rpn_box_reg: 0.0127 (0.0191) time: 1.1849 data: 0.1034 max mem: 6508 Epoch: [9] [ 20/104] eta: 0:01:36 lr: 0.000001 loss: 0.9999 (0.9711) loss_classifier: 0.4866 (0.4879) loss_box_reg: 0.4208 (0.4084) loss_objectness: 0.0473 (0.0569) loss_rpn_box_reg: 0.0161 (0.0179) time: 1.1022 data: 0.0200 max mem: 6508 Epoch: [9] [ 30/104] eta: 0:01:23 lr: 0.000001 loss: 1.0741 (1.0154) loss_classifier: 0.5166 (0.5073) loss_box_reg: 0.4718 (0.4312) loss_objectness: 0.0532 (0.0570) loss_rpn_box_reg: 0.0161 (0.0199) time: 1.0965 data: 0.0212 max mem: 6508 Epoch: [9] [ 40/104] eta: 0:01:11 lr: 0.000001 loss: 1.0208 (1.0040) loss_classifier: 0.5166 (0.5043) loss_box_reg: 0.4274 (0.4275) loss_objectness: 0.0451 (0.0524) loss_rpn_box_reg: 0.0175 (0.0197) time: 1.0804 data: 0.0244 max mem: 6508 Epoch: [9] [ 50/104] eta: 0:00:59 lr: 0.000001 loss: 0.9630 (0.9806) loss_classifier: 0.4896 (0.4954) loss_box_reg: 0.4103 (0.4152) loss_objectness: 0.0382 (0.0507) loss_rpn_box_reg: 0.0177 (0.0192) time: 1.0648 data: 0.0245 max mem: 6508 Epoch: [9] [ 60/104] eta: 0:00:48 lr: 0.000001 loss: 0.9851 (1.0015) loss_classifier: 0.5003 (0.5055) loss_box_reg: 0.4112 (0.4223) loss_objectness: 0.0423 (0.0538) loss_rpn_box_reg: 0.0188 (0.0200) time: 1.0505 data: 0.0221 max mem: 6508 Epoch: [9] [ 70/104] eta: 0:00:36 lr: 0.000001 loss: 0.9349 (0.9778) loss_classifier: 0.4848 (0.4952) loss_box_reg: 0.3944 (0.4108) loss_objectness: 0.0452 (0.0527) loss_rpn_box_reg: 0.0173 (0.0191) time: 1.0451 data: 0.0205 max mem: 6508 Epoch: [9] [ 80/104] eta: 0:00:26 lr: 0.000001 loss: 0.8820 (0.9653) loss_classifier: 0.4339 (0.4890) loss_box_reg: 0.3741 (0.4053) loss_objectness: 0.0316 (0.0527) loss_rpn_box_reg: 0.0098 (0.0183) time: 1.0563 data: 0.0219 max mem: 6508 Epoch: [9] [ 90/104] eta: 0:00:15 lr: 0.000001 loss: 0.9438 (0.9706) loss_classifier: 0.4703 (0.4897) loss_box_reg: 0.3989 (0.4069) loss_objectness: 0.0394 (0.0558) loss_rpn_box_reg: 0.0126 (0.0182) time: 1.0688 data: 0.0222 max mem: 6508 Epoch: [9] [100/104] eta: 0:00:04 lr: 0.000001 loss: 0.9698 (0.9680) loss_classifier: 0.4863 (0.4885) loss_box_reg: 0.4170 (0.4073) loss_objectness: 0.0481 (0.0544) loss_rpn_box_reg: 0.0124 (0.0177) time: 1.0696 data: 0.0201 max mem: 6508 Epoch: [9] [103/104] eta: 0:00:01 lr: 0.000001 loss: 0.9841 (0.9693) loss_classifier: 0.4922 (0.4894) loss_box_reg: 0.4309 (0.4076) loss_objectness: 0.0504 (0.0545) loss_rpn_box_reg: 0.0142 (0.0178) time: 1.0696 data: 0.0198 max mem: 6508 Epoch: [9] Total time: 0:01:52 (1.0823 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:37 model_time: 0.5051 (0.5051) evaluator_time: 0.0168 (0.0168) time: 1.4299 data: 0.8896 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4453 (0.4466) evaluator_time: 0.0289 (0.0323) time: 0.5133 data: 0.0253 max mem: 6508 Test: Total time: 0:00:14 (0.5489 s / it) Averaged stats: model_time: 0.4453 (0.4466) evaluator_time: 0.0289 (0.0323) Accumulating evaluation results... DONE (t=0.20s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.029 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.077 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.014 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.037 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.030 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.014 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.125 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.157 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025 Epoch: [10] [ 0/104] eta: 0:03:18 lr: 0.000001 loss: 0.8985 (0.8985) loss_classifier: 0.4508 (0.4508) loss_box_reg: 0.3753 (0.3753) loss_objectness: 0.0546 (0.0546) loss_rpn_box_reg: 0.0178 (0.0178) time: 1.9127 data: 0.7313 max mem: 6508 Epoch: [10] [ 10/104] eta: 0:01:51 lr: 0.000001 loss: 0.9647 (0.9517) loss_classifier: 0.4805 (0.4820) loss_box_reg: 0.4047 (0.3959) loss_objectness: 0.0524 (0.0555) loss_rpn_box_reg: 0.0178 (0.0182) time: 1.1822 data: 0.0876 max mem: 6508 Epoch: [10] [ 20/104] eta: 0:01:36 lr: 0.000001 loss: 0.8716 (0.9436) loss_classifier: 0.4560 (0.4819) loss_box_reg: 0.3817 (0.3931) loss_objectness: 0.0483 (0.0518) loss_rpn_box_reg: 0.0144 (0.0169) time: 1.1104 data: 0.0225 max mem: 6508 Epoch: [10] [ 30/104] eta: 0:01:23 lr: 0.000001 loss: 0.8716 (0.9553) loss_classifier: 0.4485 (0.4838) loss_box_reg: 0.3739 (0.3985) loss_objectness: 0.0557 (0.0558) loss_rpn_box_reg: 0.0143 (0.0172) time: 1.0981 data: 0.0211 max mem: 6508 Epoch: [10] [ 40/104] eta: 0:01:11 lr: 0.000001 loss: 0.9804 (0.9572) loss_classifier: 0.4722 (0.4811) loss_box_reg: 0.4234 (0.3979) loss_objectness: 0.0491 (0.0607) loss_rpn_box_reg: 0.0168 (0.0175) time: 1.0732 data: 0.0198 max mem: 6508 Epoch: [10] [ 50/104] eta: 0:00:59 lr: 0.000001 loss: 0.9462 (0.9525) loss_classifier: 0.4719 (0.4801) loss_box_reg: 0.3912 (0.3961) loss_objectness: 0.0443 (0.0591) loss_rpn_box_reg: 0.0157 (0.0172) time: 1.0611 data: 0.0215 max mem: 6508 Epoch: [10] [ 60/104] eta: 0:00:48 lr: 0.000001 loss: 0.9680 (0.9716) loss_classifier: 0.5065 (0.4896) loss_box_reg: 0.4100 (0.4073) loss_objectness: 0.0416 (0.0573) loss_rpn_box_reg: 0.0134 (0.0175) time: 1.0651 data: 0.0244 max mem: 6508 Epoch: [10] [ 70/104] eta: 0:00:37 lr: 0.000001 loss: 0.9544 (0.9686) loss_classifier: 0.4950 (0.4873) loss_box_reg: 0.4145 (0.4071) loss_objectness: 0.0416 (0.0567) loss_rpn_box_reg: 0.0155 (0.0175) time: 1.0577 data: 0.0222 max mem: 6508 Epoch: [10] [ 80/104] eta: 0:00:26 lr: 0.000001 loss: 0.9351 (0.9720) loss_classifier: 0.4696 (0.4891) loss_box_reg: 0.3985 (0.4081) loss_objectness: 0.0418 (0.0572) loss_rpn_box_reg: 0.0155 (0.0177) time: 1.0524 data: 0.0197 max mem: 6508 Epoch: [10] [ 90/104] eta: 0:00:15 lr: 0.000001 loss: 0.9478 (0.9635) loss_classifier: 0.4696 (0.4860) loss_box_reg: 0.3947 (0.4043) loss_objectness: 0.0492 (0.0556) loss_rpn_box_reg: 0.0123 (0.0176) time: 1.0638 data: 0.0208 max mem: 6508 Epoch: [10] [100/104] eta: 0:00:04 lr: 0.000001 loss: 0.9310 (0.9661) loss_classifier: 0.4724 (0.4876) loss_box_reg: 0.4140 (0.4064) loss_objectness: 0.0444 (0.0546) loss_rpn_box_reg: 0.0123 (0.0175) time: 1.0693 data: 0.0202 max mem: 6508 Epoch: [10] [103/104] eta: 0:00:01 lr: 0.000001 loss: 0.9491 (0.9697) loss_classifier: 0.4825 (0.4894) loss_box_reg: 0.4180 (0.4079) loss_objectness: 0.0472 (0.0545) loss_rpn_box_reg: 0.0167 (0.0178) time: 1.0690 data: 0.0190 max mem: 6508 Epoch: [10] Total time: 0:01:52 (1.0835 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.5264 (0.5264) evaluator_time: 0.0305 (0.0305) time: 1.2548 data: 0.6806 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4470 (0.4466) evaluator_time: 0.0282 (0.0290) time: 0.5045 data: 0.0221 max mem: 6508 Test: Total time: 0:00:13 (0.5355 s / it) Averaged stats: model_time: 0.4470 (0.4466) evaluator_time: 0.0282 (0.0290) Accumulating evaluation results... DONE (t=0.22s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.028 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.014 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.036 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.030 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.014 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.125 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.150 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025 Epoch: [11] [ 0/104] eta: 0:03:29 lr: 0.000001 loss: 0.7550 (0.7550) loss_classifier: 0.4027 (0.4027) loss_box_reg: 0.3002 (0.3002) loss_objectness: 0.0430 (0.0430) loss_rpn_box_reg: 0.0090 (0.0090) time: 2.0159 data: 0.9170 max mem: 6508 Epoch: [11] [ 10/104] eta: 0:01:51 lr: 0.000001 loss: 0.9419 (0.9750) loss_classifier: 0.4774 (0.4959) loss_box_reg: 0.4060 (0.4132) loss_objectness: 0.0463 (0.0481) loss_rpn_box_reg: 0.0138 (0.0178) time: 1.1869 data: 0.1023 max mem: 6508 Epoch: [11] [ 20/104] eta: 0:01:36 lr: 0.000001 loss: 1.0021 (1.0197) loss_classifier: 0.5084 (0.5174) loss_box_reg: 0.4273 (0.4354) loss_objectness: 0.0445 (0.0490) loss_rpn_box_reg: 0.0159 (0.0179) time: 1.1058 data: 0.0228 max mem: 6508 Epoch: [11] [ 30/104] eta: 0:01:23 lr: 0.000001 loss: 0.9841 (0.9790) loss_classifier: 0.5064 (0.5001) loss_box_reg: 0.4191 (0.4162) loss_objectness: 0.0389 (0.0471) loss_rpn_box_reg: 0.0142 (0.0157) time: 1.0936 data: 0.0238 max mem: 6508 Epoch: [11] [ 40/104] eta: 0:01:11 lr: 0.000001 loss: 0.8633 (0.9623) loss_classifier: 0.4275 (0.4892) loss_box_reg: 0.3595 (0.4084) loss_objectness: 0.0427 (0.0494) loss_rpn_box_reg: 0.0124 (0.0153) time: 1.0685 data: 0.0212 max mem: 6508 Epoch: [11] [ 50/104] eta: 0:00:59 lr: 0.000001 loss: 0.9035 (0.9643) loss_classifier: 0.4459 (0.4886) loss_box_reg: 0.3850 (0.4086) loss_objectness: 0.0444 (0.0513) loss_rpn_box_reg: 0.0146 (0.0158) time: 1.0535 data: 0.0200 max mem: 6508 Epoch: [11] [ 60/104] eta: 0:00:48 lr: 0.000001 loss: 0.8914 (0.9436) loss_classifier: 0.4438 (0.4784) loss_box_reg: 0.3755 (0.3993) loss_objectness: 0.0444 (0.0507) loss_rpn_box_reg: 0.0118 (0.0153) time: 1.0539 data: 0.0221 max mem: 6508 Epoch: [11] [ 70/104] eta: 0:00:36 lr: 0.000001 loss: 0.8592 (0.9572) loss_classifier: 0.4411 (0.4834) loss_box_reg: 0.3648 (0.4021) loss_objectness: 0.0479 (0.0546) loss_rpn_box_reg: 0.0142 (0.0170) time: 1.0521 data: 0.0216 max mem: 6508 Epoch: [11] [ 80/104] eta: 0:00:25 lr: 0.000001 loss: 0.8975 (0.9558) loss_classifier: 0.4754 (0.4825) loss_box_reg: 0.3745 (0.4016) loss_objectness: 0.0491 (0.0543) loss_rpn_box_reg: 0.0185 (0.0174) time: 1.0519 data: 0.0195 max mem: 6508 Epoch: [11] [ 90/104] eta: 0:00:15 lr: 0.000001 loss: 0.8975 (0.9679) loss_classifier: 0.4754 (0.4883) loss_box_reg: 0.3767 (0.4061) loss_objectness: 0.0581 (0.0557) loss_rpn_box_reg: 0.0180 (0.0178) time: 1.0667 data: 0.0204 max mem: 6508 Epoch: [11] [100/104] eta: 0:00:04 lr: 0.000001 loss: 0.9923 (0.9718) loss_classifier: 0.4808 (0.4895) loss_box_reg: 0.3905 (0.4083) loss_objectness: 0.0560 (0.0561) loss_rpn_box_reg: 0.0173 (0.0179) time: 1.0735 data: 0.0206 max mem: 6508 Epoch: [11] [103/104] eta: 0:00:01 lr: 0.000001 loss: 0.8953 (0.9701) loss_classifier: 0.4682 (0.4893) loss_box_reg: 0.3905 (0.4076) loss_objectness: 0.0488 (0.0553) loss_rpn_box_reg: 0.0153 (0.0178) time: 1.0683 data: 0.0192 max mem: 6508 Epoch: [11] Total time: 0:01:52 (1.0808 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.4939 (0.4939) evaluator_time: 0.0360 (0.0360) time: 1.2434 data: 0.6966 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4436 (0.4446) evaluator_time: 0.0245 (0.0287) time: 0.5009 data: 0.0231 max mem: 6508 Test: Total time: 0:00:14 (0.5470 s / it) Averaged stats: model_time: 0.4436 (0.4446) evaluator_time: 0.0245 (0.0287) Accumulating evaluation results... DONE (t=0.20s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.028 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.014 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.036 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.030 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.014 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.125 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.150 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025 Epoch: [12] [ 0/104] eta: 0:03:17 lr: 0.000000 loss: 1.2637 (1.2637) loss_classifier: 0.5983 (0.5983) loss_box_reg: 0.6241 (0.6241) loss_objectness: 0.0310 (0.0310) loss_rpn_box_reg: 0.0103 (0.0103) time: 1.9002 data: 0.7437 max mem: 6508 Epoch: [12] [ 10/104] eta: 0:01:50 lr: 0.000000 loss: 1.1292 (1.1048) loss_classifier: 0.5538 (0.5505) loss_box_reg: 0.4678 (0.4885) loss_objectness: 0.0361 (0.0473) loss_rpn_box_reg: 0.0126 (0.0185) time: 1.1738 data: 0.0860 max mem: 6508 Epoch: [12] [ 20/104] eta: 0:01:35 lr: 0.000000 loss: 0.9303 (1.0023) loss_classifier: 0.4809 (0.5046) loss_box_reg: 0.4035 (0.4351) loss_objectness: 0.0362 (0.0456) loss_rpn_box_reg: 0.0159 (0.0171) time: 1.1010 data: 0.0209 max mem: 6508 Epoch: [12] [ 30/104] eta: 0:01:22 lr: 0.000000 loss: 0.8486 (0.9891) loss_classifier: 0.4486 (0.4980) loss_box_reg: 0.3564 (0.4227) loss_objectness: 0.0408 (0.0495) loss_rpn_box_reg: 0.0159 (0.0189) time: 1.0926 data: 0.0215 max mem: 6508 Epoch: [12] [ 40/104] eta: 0:01:10 lr: 0.000000 loss: 0.8770 (0.9793) loss_classifier: 0.4563 (0.4950) loss_box_reg: 0.3876 (0.4167) loss_objectness: 0.0418 (0.0494) loss_rpn_box_reg: 0.0150 (0.0182) time: 1.0717 data: 0.0202 max mem: 6508 Epoch: [12] [ 50/104] eta: 0:00:59 lr: 0.000000 loss: 0.9349 (0.9718) loss_classifier: 0.4563 (0.4900) loss_box_reg: 0.3876 (0.4106) loss_objectness: 0.0422 (0.0525) loss_rpn_box_reg: 0.0155 (0.0187) time: 1.0530 data: 0.0187 max mem: 6508 Epoch: [12] [ 60/104] eta: 0:00:47 lr: 0.000000 loss: 0.8260 (0.9482) loss_classifier: 0.4230 (0.4795) loss_box_reg: 0.3398 (0.4001) loss_objectness: 0.0408 (0.0506) loss_rpn_box_reg: 0.0120 (0.0181) time: 1.0511 data: 0.0196 max mem: 6508 Epoch: [12] [ 70/104] eta: 0:00:36 lr: 0.000000 loss: 0.8678 (0.9552) loss_classifier: 0.4651 (0.4838) loss_box_reg: 0.3602 (0.4023) loss_objectness: 0.0410 (0.0511) loss_rpn_box_reg: 0.0118 (0.0180) time: 1.0573 data: 0.0204 max mem: 6508 Epoch: [12] [ 80/104] eta: 0:00:25 lr: 0.000000 loss: 0.9021 (0.9517) loss_classifier: 0.4667 (0.4829) loss_box_reg: 0.3757 (0.4006) loss_objectness: 0.0441 (0.0507) loss_rpn_box_reg: 0.0143 (0.0176) time: 1.0608 data: 0.0206 max mem: 6508 Epoch: [12] [ 90/104] eta: 0:00:15 lr: 0.000000 loss: 0.9521 (0.9610) loss_classifier: 0.4933 (0.4853) loss_box_reg: 0.3953 (0.4027) loss_objectness: 0.0518 (0.0549) loss_rpn_box_reg: 0.0153 (0.0181) time: 1.0658 data: 0.0224 max mem: 6508 Epoch: [12] [100/104] eta: 0:00:04 lr: 0.000000 loss: 0.9724 (0.9599) loss_classifier: 0.4933 (0.4856) loss_box_reg: 0.4017 (0.4031) loss_objectness: 0.0518 (0.0537) loss_rpn_box_reg: 0.0148 (0.0176) time: 1.0764 data: 0.0239 max mem: 6508 Epoch: [12] [103/104] eta: 0:00:01 lr: 0.000000 loss: 0.9875 (0.9686) loss_classifier: 0.5226 (0.4893) loss_box_reg: 0.4037 (0.4070) loss_objectness: 0.0518 (0.0545) loss_rpn_box_reg: 0.0186 (0.0178) time: 1.0762 data: 0.0228 max mem: 6508 Epoch: [12] Total time: 0:01:52 (1.0816 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.5460 (0.5460) evaluator_time: 0.0506 (0.0506) time: 1.2540 data: 0.6371 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4400 (0.4437) evaluator_time: 0.0189 (0.0318) time: 0.4944 data: 0.0212 max mem: 6508 Test: Total time: 0:00:13 (0.5340 s / it) Averaged stats: model_time: 0.4400 (0.4437) evaluator_time: 0.0189 (0.0318) Accumulating evaluation results... DONE (t=0.21s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.028 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.014 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.036 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.030 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.014 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.125 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.150 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025 Epoch: [13] [ 0/104] eta: 0:03:15 lr: 0.000000 loss: 1.0455 (1.0455) loss_classifier: 0.5254 (0.5254) loss_box_reg: 0.4590 (0.4590) loss_objectness: 0.0490 (0.0490) loss_rpn_box_reg: 0.0121 (0.0121) time: 1.8814 data: 0.6603 max mem: 6508 Epoch: [13] [ 10/104] eta: 0:01:50 lr: 0.000000 loss: 0.9119 (1.0463) loss_classifier: 0.4604 (0.5150) loss_box_reg: 0.3810 (0.4381) loss_objectness: 0.0483 (0.0701) loss_rpn_box_reg: 0.0173 (0.0231) time: 1.1755 data: 0.0781 max mem: 6508 Epoch: [13] [ 20/104] eta: 0:01:35 lr: 0.000000 loss: 1.1687 (1.1211) loss_classifier: 0.5784 (0.5539) loss_box_reg: 0.5096 (0.4756) loss_objectness: 0.0587 (0.0683) loss_rpn_box_reg: 0.0221 (0.0233) time: 1.1049 data: 0.0194 max mem: 6508 Epoch: [13] [ 30/104] eta: 0:01:23 lr: 0.000000 loss: 1.0182 (1.0474) loss_classifier: 0.4869 (0.5202) loss_box_reg: 0.4253 (0.4426) loss_objectness: 0.0570 (0.0642) loss_rpn_box_reg: 0.0168 (0.0204) time: 1.0998 data: 0.0205 max mem: 6508 Epoch: [13] [ 40/104] eta: 0:01:11 lr: 0.000000 loss: 0.8346 (1.0101) loss_classifier: 0.4342 (0.5053) loss_box_reg: 0.3361 (0.4237) loss_objectness: 0.0438 (0.0615) loss_rpn_box_reg: 0.0110 (0.0196) time: 1.0775 data: 0.0208 max mem: 6508 Epoch: [13] [ 50/104] eta: 0:00:59 lr: 0.000000 loss: 0.8370 (0.9929) loss_classifier: 0.4228 (0.4970) loss_box_reg: 0.3397 (0.4177) loss_objectness: 0.0368 (0.0584) loss_rpn_box_reg: 0.0137 (0.0198) time: 1.0545 data: 0.0194 max mem: 6508 Epoch: [13] [ 60/104] eta: 0:00:47 lr: 0.000000 loss: 0.8682 (0.9842) loss_classifier: 0.4398 (0.4952) loss_box_reg: 0.3525 (0.4129) loss_objectness: 0.0405 (0.0568) loss_rpn_box_reg: 0.0125 (0.0194) time: 1.0480 data: 0.0208 max mem: 6508 Epoch: [13] [ 70/104] eta: 0:00:36 lr: 0.000000 loss: 0.9294 (0.9796) loss_classifier: 0.4642 (0.4935) loss_box_reg: 0.3764 (0.4101) loss_objectness: 0.0451 (0.0570) loss_rpn_box_reg: 0.0128 (0.0189) time: 1.0484 data: 0.0222 max mem: 6508 Epoch: [13] [ 80/104] eta: 0:00:25 lr: 0.000000 loss: 0.9831 (0.9836) loss_classifier: 0.4986 (0.4969) loss_box_reg: 0.4131 (0.4129) loss_objectness: 0.0478 (0.0551) loss_rpn_box_reg: 0.0153 (0.0187) time: 1.0544 data: 0.0215 max mem: 6508 Epoch: [13] [ 90/104] eta: 0:00:15 lr: 0.000000 loss: 0.9340 (0.9727) loss_classifier: 0.4654 (0.4910) loss_box_reg: 0.4099 (0.4078) loss_objectness: 0.0500 (0.0554) loss_rpn_box_reg: 0.0152 (0.0185) time: 1.0612 data: 0.0203 max mem: 6508 Epoch: [13] [100/104] eta: 0:00:04 lr: 0.000000 loss: 0.9029 (0.9735) loss_classifier: 0.4607 (0.4907) loss_box_reg: 0.3724 (0.4095) loss_objectness: 0.0532 (0.0552) loss_rpn_box_reg: 0.0149 (0.0181) time: 1.0731 data: 0.0209 max mem: 6508 Epoch: [13] [103/104] eta: 0:00:01 lr: 0.000000 loss: 0.9214 (0.9691) loss_classifier: 0.4654 (0.4893) loss_box_reg: 0.3794 (0.4073) loss_objectness: 0.0520 (0.0547) loss_rpn_box_reg: 0.0129 (0.0178) time: 1.0757 data: 0.0207 max mem: 6508 Epoch: [13] Total time: 0:01:52 (1.0814 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:50 model_time: 0.6735 (0.6735) evaluator_time: 0.1359 (0.1359) time: 1.9361 data: 1.0955 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4414 (0.4503) evaluator_time: 0.0201 (0.0303) time: 0.4906 data: 0.0192 max mem: 6508 Test: Total time: 0:00:14 (0.5563 s / it) Averaged stats: model_time: 0.4414 (0.4503) evaluator_time: 0.0201 (0.0303) Accumulating evaluation results... DONE (t=0.35s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.028 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.014 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.036 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.030 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.014 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.125 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.150 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025 Epoch: [14] [ 0/104] eta: 0:04:44 lr: 0.000000 loss: 0.6101 (0.6101) loss_classifier: 0.2887 (0.2887) loss_box_reg: 0.2060 (0.2060) loss_objectness: 0.0904 (0.0904) loss_rpn_box_reg: 0.0251 (0.0251) time: 2.7340 data: 1.3935 max mem: 6508 Epoch: [14] [ 10/104] eta: 0:01:55 lr: 0.000000 loss: 0.9464 (0.9770) loss_classifier: 0.4903 (0.4883) loss_box_reg: 0.3871 (0.4116) loss_objectness: 0.0569 (0.0572) loss_rpn_box_reg: 0.0206 (0.0199) time: 1.2336 data: 0.1411 max mem: 6508 Epoch: [14] [ 20/104] eta: 0:01:38 lr: 0.000000 loss: 0.9644 (0.9994) loss_classifier: 0.4903 (0.4978) loss_box_reg: 0.4017 (0.4186) loss_objectness: 0.0541 (0.0616) loss_rpn_box_reg: 0.0206 (0.0214) time: 1.0910 data: 0.0204 max mem: 6508 Epoch: [14] [ 30/104] eta: 0:01:24 lr: 0.000000 loss: 0.9311 (0.9635) loss_classifier: 0.4634 (0.4811) loss_box_reg: 0.3893 (0.4005) loss_objectness: 0.0541 (0.0622) loss_rpn_box_reg: 0.0179 (0.0197) time: 1.0857 data: 0.0232 max mem: 6508 Epoch: [14] [ 40/104] eta: 0:01:11 lr: 0.000000 loss: 0.9757 (0.9812) loss_classifier: 0.4914 (0.4918) loss_box_reg: 0.4169 (0.4124) loss_objectness: 0.0434 (0.0582) loss_rpn_box_reg: 0.0167 (0.0188) time: 1.0682 data: 0.0206 max mem: 6508 Epoch: [14] [ 50/104] eta: 0:00:59 lr: 0.000000 loss: 1.0226 (0.9578) loss_classifier: 0.5268 (0.4829) loss_box_reg: 0.4330 (0.4021) loss_objectness: 0.0329 (0.0543) loss_rpn_box_reg: 0.0153 (0.0185) time: 1.0619 data: 0.0200 max mem: 6508 Epoch: [14] [ 60/104] eta: 0:00:48 lr: 0.000000 loss: 0.8524 (0.9526) loss_classifier: 0.4663 (0.4810) loss_box_reg: 0.3546 (0.4005) loss_objectness: 0.0433 (0.0537) loss_rpn_box_reg: 0.0134 (0.0175) time: 1.0578 data: 0.0199 max mem: 6508 Epoch: [14] [ 70/104] eta: 0:00:37 lr: 0.000000 loss: 0.9881 (0.9633) loss_classifier: 0.4924 (0.4869) loss_box_reg: 0.3968 (0.4038) loss_objectness: 0.0472 (0.0547) loss_rpn_box_reg: 0.0161 (0.0180) time: 1.0619 data: 0.0208 max mem: 6508 Epoch: [14] [ 80/104] eta: 0:00:26 lr: 0.000000 loss: 0.9881 (0.9635) loss_classifier: 0.5019 (0.4880) loss_box_reg: 0.3968 (0.4039) loss_objectness: 0.0436 (0.0536) loss_rpn_box_reg: 0.0161 (0.0180) time: 1.0679 data: 0.0220 max mem: 6508 Epoch: [14] [ 90/104] eta: 0:00:15 lr: 0.000000 loss: 1.0380 (0.9768) loss_classifier: 0.5328 (0.4938) loss_box_reg: 0.4397 (0.4100) loss_objectness: 0.0436 (0.0547) loss_rpn_box_reg: 0.0171 (0.0183) time: 1.0679 data: 0.0217 max mem: 6508 Epoch: [14] [100/104] eta: 0:00:04 lr: 0.000000 loss: 0.9153 (0.9713) loss_classifier: 0.4701 (0.4910) loss_box_reg: 0.3913 (0.4084) loss_objectness: 0.0432 (0.0538) loss_rpn_box_reg: 0.0161 (0.0181) time: 1.0648 data: 0.0199 max mem: 6508 Epoch: [14] [103/104] eta: 0:00:01 lr: 0.000000 loss: 0.9153 (0.9683) loss_classifier: 0.4701 (0.4895) loss_box_reg: 0.3900 (0.4075) loss_objectness: 0.0465 (0.0535) loss_rpn_box_reg: 0.0154 (0.0179) time: 1.0654 data: 0.0198 max mem: 6508 Epoch: [14] Total time: 0:01:53 (1.0872 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:34 model_time: 0.5303 (0.5303) evaluator_time: 0.0218 (0.0218) time: 1.3316 data: 0.7626 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4412 (0.4442) evaluator_time: 0.0285 (0.0312) time: 0.5028 data: 0.0216 max mem: 6508 Test: Total time: 0:00:13 (0.5376 s / it) Averaged stats: model_time: 0.4412 (0.4442) evaluator_time: 0.0285 (0.0312) Accumulating evaluation results... DONE (t=0.19s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.028 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.014 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.036 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.030 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.014 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.125 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.150 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025
#save adelta
import pickle
Filename = "FRCNN4adelta.pkl"
# Define the file path where you want to save the model
filename = "/content/drive/MyDrive/dataset1/FRCNN4adelta.pkl"
# Save the model to the specified file path
torch.save(model.state_dict(), filename)
# Save the Modle to file in the current working directory
with open(Filename, 'wb') as file:
pickle.dump(model, file)
# Load the Model back from file
with open(Filename, 'rb') as file:
model = pickle.load(file)
model
FasterRCNN(
(transform): GeneralizedRCNNTransform(
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
Resize(min_size=(800,), max_size=1333, mode='bilinear')
)
(backbone): BackboneWithFPN(
(body): IntermediateLayerGetter(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): FrozenBatchNorm2d(256, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(512, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(1024, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(2048, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
)
)
(fpn): FeaturePyramidNetwork(
(inner_blocks): ModuleList(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
)
(1): Conv2dNormActivation(
(0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
(2): Conv2dNormActivation(
(0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
)
(3): Conv2dNormActivation(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
)
)
(layer_blocks): ModuleList(
(0-3): 4 x Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(extra_blocks): LastLevelMaxPool()
)
)
(rpn): RegionProposalNetwork(
(anchor_generator): AnchorGenerator()
(head): RPNHead(
(conv): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
)
)
(cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
(bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
)
)
(roi_heads): RoIHeads(
(box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)
(box_head): TwoMLPHead(
(fc6): Linear(in_features=12544, out_features=1024, bias=True)
(fc7): Linear(in_features=1024, out_features=1024, bias=True)
)
(box_predictor): FastRCNNPredictor(
(cls_score): Linear(in_features=1024, out_features=11, bias=True)
(bbox_pred): Linear(in_features=1024, out_features=44, bias=True)
)
)
)
#adam_sgd
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
def hybrid_optimizer(model, params_adam, params_sgd, lr=0.001, momentum=0.9, weight_decay=0.0005, step_size=3, gamma=0.1):
# Initialize Adam optimizer for initial training
optimizer_adam = optim.Adam(params_adam, lr=lr, weight_decay=weight_decay)
# Initialize SGD optimizer for switching
optimizer_sgd = optim.SGD(params_sgd, lr=lr, momentum=momentum, weight_decay=weight_decay)
# Learning rate scheduler for SGD optimizer
lr_scheduler = StepLR(optimizer_sgd, step_size=step_size, gamma=gamma)
# Initial optimizer
optimizer = optimizer_adam
return optimizer, optimizer_adam, optimizer_sgd, lr_scheduler
# Define your model
num_classes = 11
model = get_object_detection_model(num_classes)
# Move model to the right device
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)
# Get model parameters for Adam and SGD
params_adam = [p for p in model.parameters() if p.requires_grad]
params_sgd = [p for p in model.parameters() if p.requires_grad]
# Initialize hybrid optimizer
optimizer, optimizer_adam, optimizer_sgd, lr_scheduler = hybrid_optimizer(model, params_adam, params_sgd)
# Training loop
num_epochs = 15
hybrid_results = [] # Initialize list to store hybrid technique values
is_adam = True # Flag to indicate whether the current optimizer is Adam or not
for epoch in range(num_epochs):
# Train using current optimizer
train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
# Append value of hybrid technique
hybrid_results.append(int(is_adam)) # Append 1 for Adam, 0 for SGD
# Check a condition to switch to SGD
if epoch == 5: # For example, switch to SGD after 5 epochs
optimizer = optimizer_sgd
lr_scheduler.step() # Update learning rate for SGD
is_adam = False
# Evaluate on the test dataset
evaluate(model, data_loader_test, device=device)
# Plot the results
plt.plot(hybrid_results)
plt.xlabel('Epoch')
plt.ylabel('Hybrid Technique (1 for Adam, 0 for SGD)')
plt.title('Hybrid Optimization Technique')
plt.show()
Epoch: [0] [ 0/104] eta: 0:03:56 lr: 0.000011 loss: 3.3583 (3.3583) loss_classifier: 2.5736 (2.5736) loss_box_reg: 0.4333 (0.4333) loss_objectness: 0.3133 (0.3133) loss_rpn_box_reg: 0.0381 (0.0381) time: 2.2760 data: 0.6913 max mem: 6189 Epoch: [0] [ 10/104] eta: 0:01:57 lr: 0.000108 loss: 2.3279 (2.1386) loss_classifier: 1.5769 (1.5182) loss_box_reg: 0.3590 (0.3778) loss_objectness: 0.1724 (0.2194) loss_rpn_box_reg: 0.0200 (0.0231) time: 1.2486 data: 0.0965 max mem: 6507 Epoch: [0] [ 20/104] eta: 0:01:39 lr: 0.000205 loss: 1.2090 (1.6584) loss_classifier: 0.6640 (1.1010) loss_box_reg: 0.3590 (0.3867) loss_objectness: 0.0846 (0.1480) loss_rpn_box_reg: 0.0191 (0.0227) time: 1.1307 data: 0.0314 max mem: 6507 Epoch: [0] [ 30/104] eta: 0:01:26 lr: 0.000302 loss: 0.9238 (1.3821) loss_classifier: 0.4708 (0.8761) loss_box_reg: 0.3461 (0.3638) loss_objectness: 0.0666 (0.1216) loss_rpn_box_reg: 0.0139 (0.0207) time: 1.1243 data: 0.0262 max mem: 6507 Epoch: [0] [ 40/104] eta: 0:01:13 lr: 0.000399 loss: 0.8790 (1.2982) loss_classifier: 0.4517 (0.7931) loss_box_reg: 0.3577 (0.3717) loss_objectness: 0.0476 (0.1117) loss_rpn_box_reg: 0.0140 (0.0217) time: 1.1121 data: 0.0269 max mem: 6507 Epoch: [0] [ 50/104] eta: 0:01:01 lr: 0.000496 loss: 0.9410 (1.2036) loss_classifier: 0.4517 (0.7171) loss_box_reg: 0.3279 (0.3578) loss_objectness: 0.0535 (0.1052) loss_rpn_box_reg: 0.0218 (0.0235) time: 1.0731 data: 0.0233 max mem: 6507 Epoch: [0] [ 60/104] eta: 0:00:49 lr: 0.000593 loss: 0.8082 (1.1516) loss_classifier: 0.3806 (0.6660) loss_box_reg: 0.3105 (0.3600) loss_objectness: 0.0697 (0.1014) loss_rpn_box_reg: 0.0244 (0.0241) time: 1.0451 data: 0.0211 max mem: 6507 Epoch: [0] [ 70/104] eta: 0:00:37 lr: 0.000690 loss: 0.6632 (1.0734) loss_classifier: 0.3152 (0.6125) loss_box_reg: 0.2753 (0.3432) loss_objectness: 0.0606 (0.0945) loss_rpn_box_reg: 0.0175 (0.0231) time: 1.0392 data: 0.0225 max mem: 6507 Epoch: [0] [ 80/104] eta: 0:00:26 lr: 0.000787 loss: 0.5934 (1.0429) loss_classifier: 0.3131 (0.5863) loss_box_reg: 0.2408 (0.3441) loss_objectness: 0.0489 (0.0897) loss_rpn_box_reg: 0.0122 (0.0228) time: 1.0399 data: 0.0216 max mem: 6507 Epoch: [0] [ 90/104] eta: 0:00:15 lr: 0.000884 loss: 0.8766 (1.0211) loss_classifier: 0.3554 (0.5630) loss_box_reg: 0.3787 (0.3491) loss_objectness: 0.0441 (0.0859) loss_rpn_box_reg: 0.0196 (0.0231) time: 1.0379 data: 0.0204 max mem: 6507 Epoch: [0] [100/104] eta: 0:00:04 lr: 0.000981 loss: 0.7816 (0.9918) loss_classifier: 0.3472 (0.5380) loss_box_reg: 0.3829 (0.3490) loss_objectness: 0.0430 (0.0822) loss_rpn_box_reg: 0.0200 (0.0225) time: 1.0439 data: 0.0200 max mem: 6507 Epoch: [0] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.6924 (0.9838) loss_classifier: 0.3055 (0.5306) loss_box_reg: 0.3446 (0.3494) loss_objectness: 0.0425 (0.0814) loss_rpn_box_reg: 0.0163 (0.0224) time: 1.0474 data: 0.0201 max mem: 6507 Epoch: [0] Total time: 0:01:53 (1.0872 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:34 model_time: 0.5353 (0.5353) evaluator_time: 0.0408 (0.0408) time: 1.3204 data: 0.7312 max mem: 6507 Test: [25/26] eta: 0:00:00 model_time: 0.4609 (0.4643) evaluator_time: 0.0232 (0.0326) time: 0.5161 data: 0.0198 max mem: 6507 Test: Total time: 0:00:14 (0.5556 s / it) Averaged stats: model_time: 0.4609 (0.4643) evaluator_time: 0.0232 (0.0326) Accumulating evaluation results... DONE (t=0.25s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.098 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.314 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.024 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.066 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.080 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.122 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.073 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.178 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.223 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.246 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.178 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.221 Epoch: [1] [ 0/104] eta: 0:03:22 lr: 0.001000 loss: 0.5595 (0.5595) loss_classifier: 0.2305 (0.2305) loss_box_reg: 0.2976 (0.2976) loss_objectness: 0.0189 (0.0189) loss_rpn_box_reg: 0.0124 (0.0124) time: 1.9507 data: 0.7740 max mem: 6507 Epoch: [1] [ 10/104] eta: 0:01:51 lr: 0.001000 loss: 0.9238 (0.8926) loss_classifier: 0.3809 (0.3904) loss_box_reg: 0.4200 (0.4017) loss_objectness: 0.0522 (0.0652) loss_rpn_box_reg: 0.0273 (0.0353) time: 1.1860 data: 0.0873 max mem: 6508 Epoch: [1] [ 20/104] eta: 0:01:34 lr: 0.001000 loss: 0.9238 (0.9645) loss_classifier: 0.4395 (0.4409) loss_box_reg: 0.3396 (0.3422) loss_objectness: 0.0792 (0.1439) loss_rpn_box_reg: 0.0273 (0.0375) time: 1.0846 data: 0.0192 max mem: 6508 Epoch: [1] [ 30/104] eta: 0:01:21 lr: 0.001000 loss: 0.8937 (0.9598) loss_classifier: 0.4452 (0.4378) loss_box_reg: 0.2570 (0.3153) loss_objectness: 0.1600 (0.1692) loss_rpn_box_reg: 0.0256 (0.0375) time: 1.0453 data: 0.0202 max mem: 6508 Epoch: [1] [ 40/104] eta: 0:01:08 lr: 0.001000 loss: 0.8828 (0.9770) loss_classifier: 0.4144 (0.4524) loss_box_reg: 0.2087 (0.2943) loss_objectness: 0.1780 (0.1906) loss_rpn_box_reg: 0.0408 (0.0397) time: 1.0205 data: 0.0201 max mem: 6508 Epoch: [1] [ 50/104] eta: 0:00:57 lr: 0.001000 loss: 0.8035 (0.9467) loss_classifier: 0.3222 (0.4411) loss_box_reg: 0.1765 (0.2818) loss_objectness: 0.1685 (0.1855) loss_rpn_box_reg: 0.0305 (0.0384) time: 1.0066 data: 0.0203 max mem: 6508 Epoch: [1] [ 60/104] eta: 0:00:46 lr: 0.001000 loss: 0.7846 (0.9380) loss_classifier: 0.3764 (0.4374) loss_box_reg: 0.2107 (0.2764) loss_objectness: 0.1683 (0.1855) loss_rpn_box_reg: 0.0292 (0.0387) time: 1.0037 data: 0.0221 max mem: 6508 Epoch: [1] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.7665 (0.9111) loss_classifier: 0.3365 (0.4234) loss_box_reg: 0.1780 (0.2724) loss_objectness: 0.1311 (0.1771) loss_rpn_box_reg: 0.0338 (0.0382) time: 1.0144 data: 0.0230 max mem: 6508 Epoch: [1] [ 80/104] eta: 0:00:25 lr: 0.001000 loss: 0.7328 (0.9069) loss_classifier: 0.3365 (0.4278) loss_box_reg: 0.2070 (0.2725) loss_objectness: 0.1122 (0.1698) loss_rpn_box_reg: 0.0210 (0.0367) time: 1.0243 data: 0.0219 max mem: 6508 Epoch: [1] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.8004 (0.9005) loss_classifier: 0.3582 (0.4239) loss_box_reg: 0.2739 (0.2731) loss_objectness: 0.1272 (0.1678) loss_rpn_box_reg: 0.0222 (0.0357) time: 1.0265 data: 0.0206 max mem: 6508 Epoch: [1] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.8303 (0.8894) loss_classifier: 0.3423 (0.4180) loss_box_reg: 0.2363 (0.2700) loss_objectness: 0.1451 (0.1664) loss_rpn_box_reg: 0.0222 (0.0350) time: 1.0341 data: 0.0204 max mem: 6508 Epoch: [1] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.8303 (0.8839) loss_classifier: 0.3094 (0.4124) loss_box_reg: 0.2845 (0.2710) loss_objectness: 0.1452 (0.1656) loss_rpn_box_reg: 0.0225 (0.0349) time: 1.0314 data: 0.0192 max mem: 6508 Epoch: [1] Total time: 0:01:48 (1.0433 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:30 model_time: 0.4873 (0.4873) evaluator_time: 0.0132 (0.0132) time: 1.1813 data: 0.6721 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4349 (0.4359) evaluator_time: 0.0175 (0.0435) time: 0.5049 data: 0.0208 max mem: 6508 Test: Total time: 0:00:13 (0.5350 s / it) Averaged stats: model_time: 0.4349 (0.4359) evaluator_time: 0.0175 (0.0435) Accumulating evaluation results... DONE (t=0.14s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.032 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.112 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.004 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.028 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.063 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.007 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.056 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.101 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.115 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.154 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Epoch: [2] [ 0/104] eta: 0:03:12 lr: 0.001000 loss: 0.5037 (0.5037) loss_classifier: 0.2505 (0.2505) loss_box_reg: 0.1813 (0.1813) loss_objectness: 0.0628 (0.0628) loss_rpn_box_reg: 0.0091 (0.0091) time: 1.8531 data: 0.7893 max mem: 6508 Epoch: [2] [ 10/104] eta: 0:01:45 lr: 0.001000 loss: 0.7679 (0.7480) loss_classifier: 0.2630 (0.3047) loss_box_reg: 0.3321 (0.2946) loss_objectness: 0.1093 (0.1169) loss_rpn_box_reg: 0.0268 (0.0318) time: 1.1201 data: 0.0887 max mem: 6508 Epoch: [2] [ 20/104] eta: 0:01:31 lr: 0.001000 loss: 0.7679 (0.8105) loss_classifier: 0.3391 (0.3457) loss_box_reg: 0.3321 (0.3236) loss_objectness: 0.1086 (0.1076) loss_rpn_box_reg: 0.0280 (0.0336) time: 1.0487 data: 0.0201 max mem: 6508 Epoch: [2] [ 30/104] eta: 0:01:19 lr: 0.001000 loss: 0.6997 (0.7948) loss_classifier: 0.3233 (0.3411) loss_box_reg: 0.2844 (0.3074) loss_objectness: 0.1138 (0.1135) loss_rpn_box_reg: 0.0254 (0.0328) time: 1.0460 data: 0.0213 max mem: 6508 Epoch: [2] [ 40/104] eta: 0:01:07 lr: 0.001000 loss: 0.6524 (0.7697) loss_classifier: 0.3024 (0.3320) loss_box_reg: 0.2380 (0.3007) loss_objectness: 0.0968 (0.1053) loss_rpn_box_reg: 0.0232 (0.0317) time: 1.0326 data: 0.0201 max mem: 6508 Epoch: [2] [ 50/104] eta: 0:00:57 lr: 0.001000 loss: 0.6844 (0.7759) loss_classifier: 0.3101 (0.3390) loss_box_reg: 0.2446 (0.3037) loss_objectness: 0.0753 (0.1016) loss_rpn_box_reg: 0.0267 (0.0317) time: 1.0467 data: 0.0246 max mem: 6508 Epoch: [2] [ 60/104] eta: 0:00:46 lr: 0.001000 loss: 0.6844 (0.7725) loss_classifier: 0.3198 (0.3358) loss_box_reg: 0.2572 (0.3075) loss_objectness: 0.0772 (0.0983) loss_rpn_box_reg: 0.0240 (0.0309) time: 1.0442 data: 0.0251 max mem: 6508 Epoch: [2] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.7528 (0.7731) loss_classifier: 0.3344 (0.3377) loss_box_reg: 0.3064 (0.3110) loss_objectness: 0.0596 (0.0942) loss_rpn_box_reg: 0.0235 (0.0301) time: 1.0312 data: 0.0238 max mem: 6508 Epoch: [2] [ 80/104] eta: 0:00:25 lr: 0.001000 loss: 0.8539 (0.7948) loss_classifier: 0.3837 (0.3514) loss_box_reg: 0.3581 (0.3217) loss_objectness: 0.0577 (0.0909) loss_rpn_box_reg: 0.0265 (0.0308) time: 1.0315 data: 0.0231 max mem: 6508 Epoch: [2] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.8539 (0.7890) loss_classifier: 0.3838 (0.3490) loss_box_reg: 0.3581 (0.3225) loss_objectness: 0.0537 (0.0868) loss_rpn_box_reg: 0.0274 (0.0306) time: 1.0237 data: 0.0194 max mem: 6508 Epoch: [2] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.6873 (0.7845) loss_classifier: 0.2968 (0.3464) loss_box_reg: 0.2919 (0.3230) loss_objectness: 0.0533 (0.0849) loss_rpn_box_reg: 0.0234 (0.0302) time: 1.0292 data: 0.0194 max mem: 6508 Epoch: [2] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.6611 (0.7760) loss_classifier: 0.2968 (0.3432) loss_box_reg: 0.2749 (0.3188) loss_objectness: 0.0537 (0.0843) loss_rpn_box_reg: 0.0180 (0.0297) time: 1.0298 data: 0.0200 max mem: 6508 Epoch: [2] Total time: 0:01:48 (1.0476 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.5254 (0.5254) evaluator_time: 0.0422 (0.0422) time: 1.2660 data: 0.6837 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4378 (0.4407) evaluator_time: 0.0279 (0.0307) time: 0.4987 data: 0.0235 max mem: 6508 Test: Total time: 0:00:13 (0.5316 s / it) Averaged stats: model_time: 0.4378 (0.4407) evaluator_time: 0.0279 (0.0307) Accumulating evaluation results... DONE (t=0.17s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.095 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.293 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.024 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.101 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.115 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.045 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.174 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.278 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.090 Epoch: [3] [ 0/104] eta: 0:03:13 lr: 0.001000 loss: 0.9499 (0.9499) loss_classifier: 0.3417 (0.3417) loss_box_reg: 0.4621 (0.4621) loss_objectness: 0.1011 (0.1011) loss_rpn_box_reg: 0.0450 (0.0450) time: 1.8584 data: 0.8103 max mem: 6508 Epoch: [3] [ 10/104] eta: 0:01:47 lr: 0.001000 loss: 0.8302 (0.8235) loss_classifier: 0.3417 (0.3547) loss_box_reg: 0.3616 (0.3747) loss_objectness: 0.0448 (0.0643) loss_rpn_box_reg: 0.0273 (0.0298) time: 1.1402 data: 0.0937 max mem: 6508 Epoch: [3] [ 20/104] eta: 0:01:32 lr: 0.001000 loss: 0.6848 (0.7629) loss_classifier: 0.3111 (0.3349) loss_box_reg: 0.3178 (0.3431) loss_objectness: 0.0435 (0.0587) loss_rpn_box_reg: 0.0234 (0.0263) time: 1.0628 data: 0.0214 max mem: 6508 Epoch: [3] [ 30/104] eta: 0:01:19 lr: 0.001000 loss: 0.7425 (0.7528) loss_classifier: 0.3458 (0.3322) loss_box_reg: 0.3276 (0.3383) loss_objectness: 0.0473 (0.0571) loss_rpn_box_reg: 0.0217 (0.0252) time: 1.0461 data: 0.0202 max mem: 6508 Epoch: [3] [ 40/104] eta: 0:01:08 lr: 0.001000 loss: 0.7538 (0.7365) loss_classifier: 0.2820 (0.3206) loss_box_reg: 0.3276 (0.3360) loss_objectness: 0.0410 (0.0545) loss_rpn_box_reg: 0.0217 (0.0255) time: 1.0305 data: 0.0193 max mem: 6508 Epoch: [3] [ 50/104] eta: 0:00:57 lr: 0.001000 loss: 0.6383 (0.7350) loss_classifier: 0.2502 (0.3181) loss_box_reg: 0.3137 (0.3374) loss_objectness: 0.0410 (0.0530) loss_rpn_box_reg: 0.0182 (0.0266) time: 1.0231 data: 0.0199 max mem: 6508 Epoch: [3] [ 60/104] eta: 0:00:46 lr: 0.001000 loss: 0.6383 (0.7166) loss_classifier: 0.2553 (0.3114) loss_box_reg: 0.2984 (0.3277) loss_objectness: 0.0440 (0.0519) loss_rpn_box_reg: 0.0172 (0.0255) time: 1.0162 data: 0.0202 max mem: 6508 Epoch: [3] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.5951 (0.7024) loss_classifier: 0.2605 (0.3047) loss_box_reg: 0.2664 (0.3204) loss_objectness: 0.0458 (0.0515) loss_rpn_box_reg: 0.0172 (0.0258) time: 1.0147 data: 0.0198 max mem: 6508 Epoch: [3] [ 80/104] eta: 0:00:25 lr: 0.001000 loss: 0.6758 (0.7072) loss_classifier: 0.2742 (0.3053) loss_box_reg: 0.3144 (0.3232) loss_objectness: 0.0472 (0.0520) loss_rpn_box_reg: 0.0216 (0.0266) time: 1.0216 data: 0.0210 max mem: 6508 Epoch: [3] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.6085 (0.6951) loss_classifier: 0.2477 (0.2973) loss_box_reg: 0.3144 (0.3206) loss_objectness: 0.0452 (0.0510) loss_rpn_box_reg: 0.0232 (0.0263) time: 1.0288 data: 0.0217 max mem: 6508 Epoch: [3] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.5904 (0.6938) loss_classifier: 0.2427 (0.2959) loss_box_reg: 0.2899 (0.3212) loss_objectness: 0.0452 (0.0511) loss_rpn_box_reg: 0.0188 (0.0257) time: 1.0302 data: 0.0201 max mem: 6508 Epoch: [3] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.6085 (0.6953) loss_classifier: 0.2726 (0.2968) loss_box_reg: 0.3100 (0.3219) loss_objectness: 0.0410 (0.0507) loss_rpn_box_reg: 0.0193 (0.0259) time: 1.0272 data: 0.0186 max mem: 6508 Epoch: [3] Total time: 0:01:48 (1.0411 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.5188 (0.5188) evaluator_time: 0.0565 (0.0565) time: 1.2508 data: 0.6631 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4385 (0.4402) evaluator_time: 0.0283 (0.0381) time: 0.5035 data: 0.0237 max mem: 6508 Test: Total time: 0:00:13 (0.5367 s / it) Averaged stats: model_time: 0.4385 (0.4402) evaluator_time: 0.0283 (0.0381) Accumulating evaluation results... DONE (t=0.17s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.173 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.455 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.059 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.208 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.071 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.259 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.334 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.374 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.060 Epoch: [4] [ 0/104] eta: 0:03:07 lr: 0.001000 loss: 0.9558 (0.9558) loss_classifier: 0.4930 (0.4930) loss_box_reg: 0.3486 (0.3486) loss_objectness: 0.0946 (0.0946) loss_rpn_box_reg: 0.0195 (0.0195) time: 1.8037 data: 0.7351 max mem: 6508 Epoch: [4] [ 10/104] eta: 0:01:45 lr: 0.001000 loss: 0.7101 (0.6399) loss_classifier: 0.2657 (0.2640) loss_box_reg: 0.3435 (0.3079) loss_objectness: 0.0472 (0.0477) loss_rpn_box_reg: 0.0195 (0.0202) time: 1.1204 data: 0.0832 max mem: 6508 Epoch: [4] [ 20/104] eta: 0:01:32 lr: 0.001000 loss: 0.6309 (0.6270) loss_classifier: 0.2392 (0.2552) loss_box_reg: 0.2930 (0.3038) loss_objectness: 0.0401 (0.0461) loss_rpn_box_reg: 0.0210 (0.0220) time: 1.0610 data: 0.0199 max mem: 6508 Epoch: [4] [ 30/104] eta: 0:01:19 lr: 0.001000 loss: 0.6323 (0.6352) loss_classifier: 0.2409 (0.2580) loss_box_reg: 0.2970 (0.3073) loss_objectness: 0.0392 (0.0461) loss_rpn_box_reg: 0.0210 (0.0238) time: 1.0517 data: 0.0202 max mem: 6508 Epoch: [4] [ 40/104] eta: 0:01:08 lr: 0.001000 loss: 0.6639 (0.6324) loss_classifier: 0.2750 (0.2566) loss_box_reg: 0.2970 (0.3055) loss_objectness: 0.0370 (0.0458) loss_rpn_box_reg: 0.0238 (0.0244) time: 1.0370 data: 0.0202 max mem: 6508 Epoch: [4] [ 50/104] eta: 0:00:57 lr: 0.001000 loss: 0.5750 (0.6164) loss_classifier: 0.2283 (0.2477) loss_box_reg: 0.2813 (0.3012) loss_objectness: 0.0317 (0.0434) loss_rpn_box_reg: 0.0209 (0.0241) time: 1.0452 data: 0.0273 max mem: 6508 Epoch: [4] [ 60/104] eta: 0:00:46 lr: 0.001000 loss: 0.6330 (0.6248) loss_classifier: 0.2227 (0.2506) loss_box_reg: 0.2909 (0.3078) loss_objectness: 0.0316 (0.0428) loss_rpn_box_reg: 0.0194 (0.0236) time: 1.0604 data: 0.0331 max mem: 6508 Epoch: [4] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.6562 (0.6291) loss_classifier: 0.2589 (0.2515) loss_box_reg: 0.3541 (0.3110) loss_objectness: 0.0412 (0.0432) loss_rpn_box_reg: 0.0197 (0.0235) time: 1.0427 data: 0.0264 max mem: 6508 Epoch: [4] [ 80/104] eta: 0:00:25 lr: 0.001000 loss: 0.6114 (0.6299) loss_classifier: 0.2353 (0.2526) loss_box_reg: 0.2933 (0.3100) loss_objectness: 0.0382 (0.0435) loss_rpn_box_reg: 0.0213 (0.0237) time: 1.0201 data: 0.0193 max mem: 6508 Epoch: [4] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.5727 (0.6264) loss_classifier: 0.2379 (0.2512) loss_box_reg: 0.2812 (0.3085) loss_objectness: 0.0370 (0.0428) loss_rpn_box_reg: 0.0217 (0.0239) time: 1.0289 data: 0.0199 max mem: 6508 Epoch: [4] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.5472 (0.6211) loss_classifier: 0.2376 (0.2497) loss_box_reg: 0.2812 (0.3055) loss_objectness: 0.0378 (0.0427) loss_rpn_box_reg: 0.0183 (0.0233) time: 1.0302 data: 0.0198 max mem: 6508 Epoch: [4] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.5416 (0.6168) loss_classifier: 0.2112 (0.2477) loss_box_reg: 0.2812 (0.3037) loss_objectness: 0.0353 (0.0423) loss_rpn_box_reg: 0.0182 (0.0230) time: 1.0283 data: 0.0193 max mem: 6508 Epoch: [4] Total time: 0:01:49 (1.0498 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:32 model_time: 0.5054 (0.5054) evaluator_time: 0.0618 (0.0618) time: 1.2496 data: 0.6658 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4377 (0.4409) evaluator_time: 0.0372 (0.0436) time: 0.5433 data: 0.0468 max mem: 6508 Test: Total time: 0:00:14 (0.5649 s / it) Averaged stats: model_time: 0.4377 (0.4409) evaluator_time: 0.0372 (0.0436) Accumulating evaluation results... DONE (t=0.47s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.225 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.524 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.150 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.170 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.108 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.105 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.315 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.371 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.373 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.159 Epoch: [5] [ 0/104] eta: 0:03:42 lr: 0.001000 loss: 0.4680 (0.4680) loss_classifier: 0.1478 (0.1478) loss_box_reg: 0.2861 (0.2861) loss_objectness: 0.0217 (0.0217) loss_rpn_box_reg: 0.0124 (0.0124) time: 2.1373 data: 0.9660 max mem: 6508 Epoch: [5] [ 10/104] eta: 0:01:50 lr: 0.001000 loss: 0.4746 (0.5283) loss_classifier: 0.2071 (0.2146) loss_box_reg: 0.2416 (0.2675) loss_objectness: 0.0250 (0.0293) loss_rpn_box_reg: 0.0146 (0.0170) time: 1.1784 data: 0.1125 max mem: 6508 Epoch: [5] [ 20/104] eta: 0:01:35 lr: 0.001000 loss: 0.5318 (0.5453) loss_classifier: 0.2175 (0.2221) loss_box_reg: 0.2739 (0.2739) loss_objectness: 0.0252 (0.0321) loss_rpn_box_reg: 0.0154 (0.0172) time: 1.0828 data: 0.0252 max mem: 6508 Epoch: [5] [ 30/104] eta: 0:01:21 lr: 0.001000 loss: 0.6038 (0.5736) loss_classifier: 0.2350 (0.2375) loss_box_reg: 0.2890 (0.2788) loss_objectness: 0.0325 (0.0391) loss_rpn_box_reg: 0.0162 (0.0182) time: 1.0645 data: 0.0229 max mem: 6508 Epoch: [5] [ 40/104] eta: 0:01:09 lr: 0.001000 loss: 0.5810 (0.5703) loss_classifier: 0.2350 (0.2362) loss_box_reg: 0.2642 (0.2782) loss_objectness: 0.0323 (0.0385) loss_rpn_box_reg: 0.0140 (0.0174) time: 1.0346 data: 0.0205 max mem: 6508 Epoch: [5] [ 50/104] eta: 0:00:57 lr: 0.001000 loss: 0.5266 (0.5752) loss_classifier: 0.2094 (0.2369) loss_box_reg: 0.2642 (0.2821) loss_objectness: 0.0310 (0.0368) loss_rpn_box_reg: 0.0175 (0.0193) time: 1.0244 data: 0.0204 max mem: 6508 Epoch: [5] [ 60/104] eta: 0:00:46 lr: 0.001000 loss: 0.5092 (0.5591) loss_classifier: 0.1943 (0.2278) loss_box_reg: 0.2546 (0.2764) loss_objectness: 0.0306 (0.0361) loss_rpn_box_reg: 0.0192 (0.0188) time: 1.0229 data: 0.0221 max mem: 6508 Epoch: [5] [ 70/104] eta: 0:00:36 lr: 0.001000 loss: 0.4859 (0.5504) loss_classifier: 0.1813 (0.2209) loss_box_reg: 0.2525 (0.2763) loss_objectness: 0.0248 (0.0347) loss_rpn_box_reg: 0.0164 (0.0185) time: 1.0264 data: 0.0248 max mem: 6508 Epoch: [5] [ 80/104] eta: 0:00:25 lr: 0.001000 loss: 0.5286 (0.5521) loss_classifier: 0.1831 (0.2182) loss_box_reg: 0.2902 (0.2812) loss_objectness: 0.0236 (0.0337) loss_rpn_box_reg: 0.0197 (0.0190) time: 1.0305 data: 0.0253 max mem: 6508 Epoch: [5] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.5525 (0.5546) loss_classifier: 0.1898 (0.2181) loss_box_reg: 0.3005 (0.2828) loss_objectness: 0.0242 (0.0339) loss_rpn_box_reg: 0.0217 (0.0198) time: 1.0311 data: 0.0222 max mem: 6508 Epoch: [5] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.5126 (0.5501) loss_classifier: 0.1921 (0.2168) loss_box_reg: 0.2651 (0.2804) loss_objectness: 0.0218 (0.0332) loss_rpn_box_reg: 0.0170 (0.0197) time: 1.0316 data: 0.0204 max mem: 6508 Epoch: [5] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.4816 (0.5490) loss_classifier: 0.1829 (0.2162) loss_box_reg: 0.2651 (0.2799) loss_objectness: 0.0218 (0.0333) loss_rpn_box_reg: 0.0170 (0.0196) time: 1.0310 data: 0.0202 max mem: 6508 Epoch: [5] Total time: 0:01:49 (1.0521 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:35 model_time: 0.4859 (0.4859) evaluator_time: 0.0420 (0.0420) time: 1.3520 data: 0.8006 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4382 (0.4394) evaluator_time: 0.0253 (0.0298) time: 0.5024 data: 0.0250 max mem: 6508 Test: Total time: 0:00:13 (0.5345 s / it) Averaged stats: model_time: 0.4382 (0.4394) evaluator_time: 0.0253 (0.0298) Accumulating evaluation results... DONE (t=0.17s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.290 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.654 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.201 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.218 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.335 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.190 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.123 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.375 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.441 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.504 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.237 Epoch: [6] [ 0/104] eta: 0:03:03 lr: 0.001000 loss: 0.2004 (0.2004) loss_classifier: 0.0850 (0.0850) loss_box_reg: 0.0903 (0.0903) loss_objectness: 0.0186 (0.0186) loss_rpn_box_reg: 0.0066 (0.0066) time: 1.7673 data: 0.6507 max mem: 6508 Epoch: [6] [ 10/104] eta: 0:01:46 lr: 0.001000 loss: 0.3790 (0.4026) loss_classifier: 0.1573 (0.1662) loss_box_reg: 0.1895 (0.2015) loss_objectness: 0.0203 (0.0207) loss_rpn_box_reg: 0.0126 (0.0142) time: 1.1316 data: 0.0830 max mem: 6508 Epoch: [6] [ 20/104] eta: 0:01:31 lr: 0.001000 loss: 0.3790 (0.4043) loss_classifier: 0.1573 (0.1620) loss_box_reg: 0.2113 (0.2048) loss_objectness: 0.0220 (0.0234) loss_rpn_box_reg: 0.0126 (0.0142) time: 1.0554 data: 0.0230 max mem: 6508 Epoch: [6] [ 30/104] eta: 0:01:19 lr: 0.001000 loss: 0.3963 (0.4200) loss_classifier: 0.1639 (0.1660) loss_box_reg: 0.2222 (0.2154) loss_objectness: 0.0230 (0.0232) loss_rpn_box_reg: 0.0141 (0.0154) time: 1.0335 data: 0.0191 max mem: 6508 Epoch: [6] [ 40/104] eta: 0:01:07 lr: 0.001000 loss: 0.4557 (0.4215) loss_classifier: 0.1723 (0.1667) loss_box_reg: 0.2258 (0.2188) loss_objectness: 0.0183 (0.0212) loss_rpn_box_reg: 0.0150 (0.0149) time: 1.0207 data: 0.0190 max mem: 6508 Epoch: [6] [ 50/104] eta: 0:00:56 lr: 0.001000 loss: 0.4646 (0.4297) loss_classifier: 0.1774 (0.1703) loss_box_reg: 0.2445 (0.2235) loss_objectness: 0.0139 (0.0200) loss_rpn_box_reg: 0.0146 (0.0160) time: 1.0178 data: 0.0224 max mem: 6508 Epoch: [6] [ 60/104] eta: 0:00:45 lr: 0.001000 loss: 0.4693 (0.4323) loss_classifier: 0.1746 (0.1713) loss_box_reg: 0.2554 (0.2241) loss_objectness: 0.0151 (0.0204) loss_rpn_box_reg: 0.0173 (0.0166) time: 1.0160 data: 0.0231 max mem: 6508 Epoch: [6] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.4249 (0.4347) loss_classifier: 0.1655 (0.1703) loss_box_reg: 0.2321 (0.2279) loss_objectness: 0.0179 (0.0199) loss_rpn_box_reg: 0.0166 (0.0166) time: 1.0130 data: 0.0207 max mem: 6508 Epoch: [6] [ 80/104] eta: 0:00:24 lr: 0.001000 loss: 0.4432 (0.4389) loss_classifier: 0.1613 (0.1696) loss_box_reg: 0.2495 (0.2335) loss_objectness: 0.0150 (0.0193) loss_rpn_box_reg: 0.0130 (0.0164) time: 1.0174 data: 0.0218 max mem: 6508 Epoch: [6] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.4432 (0.4411) loss_classifier: 0.1654 (0.1694) loss_box_reg: 0.2633 (0.2356) loss_objectness: 0.0155 (0.0195) loss_rpn_box_reg: 0.0130 (0.0166) time: 1.0281 data: 0.0234 max mem: 6508 Epoch: [6] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.3908 (0.4347) loss_classifier: 0.1517 (0.1675) loss_box_reg: 0.2001 (0.2324) loss_objectness: 0.0155 (0.0190) loss_rpn_box_reg: 0.0096 (0.0158) time: 1.0319 data: 0.0225 max mem: 6508 Epoch: [6] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.3908 (0.4337) loss_classifier: 0.1499 (0.1669) loss_box_reg: 0.2096 (0.2322) loss_objectness: 0.0147 (0.0188) loss_rpn_box_reg: 0.0106 (0.0158) time: 1.0323 data: 0.0220 max mem: 6508 Epoch: [6] Total time: 0:01:47 (1.0369 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:33 model_time: 0.5286 (0.5286) evaluator_time: 0.0611 (0.0611) time: 1.2888 data: 0.6926 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4329 (0.4359) evaluator_time: 0.0202 (0.0268) time: 0.4862 data: 0.0207 max mem: 6508 Test: Total time: 0:00:13 (0.5192 s / it) Averaged stats: model_time: 0.4329 (0.4359) evaluator_time: 0.0202 (0.0268) Accumulating evaluation results... DONE (t=0.16s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.347 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.691 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.333 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.406 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.231 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.153 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.433 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.413 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.553 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.329 Epoch: [7] [ 0/104] eta: 0:03:26 lr: 0.001000 loss: 0.5793 (0.5793) loss_classifier: 0.2718 (0.2718) loss_box_reg: 0.2628 (0.2628) loss_objectness: 0.0261 (0.0261) loss_rpn_box_reg: 0.0186 (0.0186) time: 1.9895 data: 0.9524 max mem: 6508 Epoch: [7] [ 10/104] eta: 0:01:46 lr: 0.001000 loss: 0.3872 (0.4365) loss_classifier: 0.1535 (0.1738) loss_box_reg: 0.2187 (0.2288) loss_objectness: 0.0147 (0.0176) loss_rpn_box_reg: 0.0158 (0.0163) time: 1.1289 data: 0.1037 max mem: 6508 Epoch: [7] [ 20/104] eta: 0:01:32 lr: 0.001000 loss: 0.3806 (0.4099) loss_classifier: 0.1420 (0.1582) loss_box_reg: 0.2103 (0.2188) loss_objectness: 0.0147 (0.0185) loss_rpn_box_reg: 0.0118 (0.0143) time: 1.0520 data: 0.0194 max mem: 6508 Epoch: [7] [ 30/104] eta: 0:01:19 lr: 0.001000 loss: 0.4133 (0.4081) loss_classifier: 0.1405 (0.1565) loss_box_reg: 0.2212 (0.2168) loss_objectness: 0.0172 (0.0196) loss_rpn_box_reg: 0.0121 (0.0152) time: 1.0527 data: 0.0213 max mem: 6508 Epoch: [7] [ 40/104] eta: 0:01:08 lr: 0.001000 loss: 0.3974 (0.3959) loss_classifier: 0.1418 (0.1503) loss_box_reg: 0.1994 (0.2121) loss_objectness: 0.0180 (0.0193) loss_rpn_box_reg: 0.0115 (0.0142) time: 1.0371 data: 0.0224 max mem: 6508 Epoch: [7] [ 50/104] eta: 0:00:57 lr: 0.001000 loss: 0.3500 (0.3964) loss_classifier: 0.1315 (0.1487) loss_box_reg: 0.1885 (0.2147) loss_objectness: 0.0163 (0.0185) loss_rpn_box_reg: 0.0110 (0.0145) time: 1.0241 data: 0.0218 max mem: 6508 Epoch: [7] [ 60/104] eta: 0:00:46 lr: 0.001000 loss: 0.3600 (0.3941) loss_classifier: 0.1315 (0.1487) loss_box_reg: 0.2080 (0.2129) loss_objectness: 0.0157 (0.0183) loss_rpn_box_reg: 0.0108 (0.0142) time: 1.0117 data: 0.0209 max mem: 6508 Epoch: [7] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.3652 (0.3967) loss_classifier: 0.1331 (0.1506) loss_box_reg: 0.2129 (0.2147) loss_objectness: 0.0148 (0.0177) loss_rpn_box_reg: 0.0103 (0.0137) time: 1.0107 data: 0.0219 max mem: 6508 Epoch: [7] [ 80/104] eta: 0:00:25 lr: 0.001000 loss: 0.3829 (0.3960) loss_classifier: 0.1563 (0.1501) loss_box_reg: 0.2107 (0.2148) loss_objectness: 0.0132 (0.0172) loss_rpn_box_reg: 0.0103 (0.0139) time: 1.0236 data: 0.0241 max mem: 6508 Epoch: [7] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.4413 (0.4044) loss_classifier: 0.1581 (0.1521) loss_box_reg: 0.2467 (0.2207) loss_objectness: 0.0141 (0.0171) loss_rpn_box_reg: 0.0127 (0.0145) time: 1.0289 data: 0.0229 max mem: 6508 Epoch: [7] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.4413 (0.4047) loss_classifier: 0.1590 (0.1522) loss_box_reg: 0.2542 (0.2211) loss_objectness: 0.0126 (0.0169) loss_rpn_box_reg: 0.0147 (0.0144) time: 1.0227 data: 0.0199 max mem: 6508 Epoch: [7] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.4344 (0.4043) loss_classifier: 0.1520 (0.1520) loss_box_reg: 0.2392 (0.2209) loss_objectness: 0.0126 (0.0171) loss_rpn_box_reg: 0.0147 (0.0143) time: 1.0264 data: 0.0201 max mem: 6508 Epoch: [7] Total time: 0:01:48 (1.0407 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:41 model_time: 0.4911 (0.4911) evaluator_time: 0.0342 (0.0342) time: 1.6021 data: 1.0465 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4356 (0.4383) evaluator_time: 0.0215 (0.0262) time: 0.4905 data: 0.0205 max mem: 6508 Test: Total time: 0:00:14 (0.5396 s / it) Averaged stats: model_time: 0.4356 (0.4383) evaluator_time: 0.0215 (0.0262) Accumulating evaluation results... DONE (t=0.33s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.355 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.700 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.315 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.288 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.406 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.244 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.157 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.442 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.506 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.434 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.532 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.317 Epoch: [8] [ 0/104] eta: 0:04:34 lr: 0.001000 loss: 0.2473 (0.2473) loss_classifier: 0.0932 (0.0932) loss_box_reg: 0.1216 (0.1216) loss_objectness: 0.0231 (0.0231) loss_rpn_box_reg: 0.0095 (0.0095) time: 2.6393 data: 1.2524 max mem: 6508 Epoch: [8] [ 10/104] eta: 0:01:52 lr: 0.001000 loss: 0.3371 (0.4016) loss_classifier: 0.1366 (0.1563) loss_box_reg: 0.1766 (0.2157) loss_objectness: 0.0176 (0.0170) loss_rpn_box_reg: 0.0103 (0.0127) time: 1.2014 data: 0.1328 max mem: 6508 Epoch: [8] [ 20/104] eta: 0:01:35 lr: 0.001000 loss: 0.3752 (0.3958) loss_classifier: 0.1365 (0.1530) loss_box_reg: 0.1974 (0.2131) loss_objectness: 0.0141 (0.0163) loss_rpn_box_reg: 0.0125 (0.0134) time: 1.0572 data: 0.0211 max mem: 6508 Epoch: [8] [ 30/104] eta: 0:01:22 lr: 0.001000 loss: 0.3752 (0.3949) loss_classifier: 0.1365 (0.1489) loss_box_reg: 0.2118 (0.2171) loss_objectness: 0.0131 (0.0151) loss_rpn_box_reg: 0.0138 (0.0137) time: 1.0575 data: 0.0232 max mem: 6508 Epoch: [8] [ 40/104] eta: 0:01:09 lr: 0.001000 loss: 0.3710 (0.3867) loss_classifier: 0.1305 (0.1442) loss_box_reg: 0.2202 (0.2145) loss_objectness: 0.0123 (0.0149) loss_rpn_box_reg: 0.0119 (0.0131) time: 1.0413 data: 0.0244 max mem: 6508 Epoch: [8] [ 50/104] eta: 0:00:57 lr: 0.001000 loss: 0.3710 (0.3918) loss_classifier: 0.1323 (0.1467) loss_box_reg: 0.2105 (0.2161) loss_objectness: 0.0123 (0.0154) loss_rpn_box_reg: 0.0106 (0.0136) time: 1.0161 data: 0.0213 max mem: 6508 Epoch: [8] [ 60/104] eta: 0:00:46 lr: 0.001000 loss: 0.3706 (0.3911) loss_classifier: 0.1415 (0.1471) loss_box_reg: 0.1955 (0.2153) loss_objectness: 0.0110 (0.0150) loss_rpn_box_reg: 0.0138 (0.0138) time: 1.0134 data: 0.0206 max mem: 6508 Epoch: [8] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.3706 (0.3889) loss_classifier: 0.1418 (0.1461) loss_box_reg: 0.1955 (0.2142) loss_objectness: 0.0110 (0.0150) loss_rpn_box_reg: 0.0138 (0.0136) time: 1.0187 data: 0.0216 max mem: 6508 Epoch: [8] [ 80/104] eta: 0:00:25 lr: 0.001000 loss: 0.3642 (0.3899) loss_classifier: 0.1418 (0.1457) loss_box_reg: 0.2009 (0.2146) loss_objectness: 0.0159 (0.0154) loss_rpn_box_reg: 0.0133 (0.0141) time: 1.0195 data: 0.0208 max mem: 6508 Epoch: [8] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.3939 (0.3970) loss_classifier: 0.1465 (0.1479) loss_box_reg: 0.2182 (0.2190) loss_objectness: 0.0168 (0.0160) loss_rpn_box_reg: 0.0131 (0.0141) time: 1.0206 data: 0.0206 max mem: 6508 Epoch: [8] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.3779 (0.3900) loss_classifier: 0.1397 (0.1455) loss_box_reg: 0.2145 (0.2154) loss_objectness: 0.0126 (0.0156) loss_rpn_box_reg: 0.0105 (0.0136) time: 1.0246 data: 0.0208 max mem: 6508 Epoch: [8] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.3779 (0.3899) loss_classifier: 0.1397 (0.1455) loss_box_reg: 0.2145 (0.2152) loss_objectness: 0.0129 (0.0156) loss_rpn_box_reg: 0.0105 (0.0136) time: 1.0223 data: 0.0200 max mem: 6508 Epoch: [8] Total time: 0:01:48 (1.0475 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:40 model_time: 0.6768 (0.6768) evaluator_time: 0.1020 (0.1020) time: 1.5605 data: 0.7592 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4348 (0.4439) evaluator_time: 0.0188 (0.0410) time: 0.4799 data: 0.0191 max mem: 6508 Test: Total time: 0:00:14 (0.5454 s / it) Averaged stats: model_time: 0.4348 (0.4439) evaluator_time: 0.0188 (0.0410) Accumulating evaluation results... DONE (t=0.14s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.353 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.710 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.311 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.299 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.416 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.154 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.438 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.494 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.441 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.533 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.307 Epoch: [9] [ 0/104] eta: 0:03:51 lr: 0.001000 loss: 0.2582 (0.2582) loss_classifier: 0.0994 (0.0994) loss_box_reg: 0.1455 (0.1455) loss_objectness: 0.0069 (0.0069) loss_rpn_box_reg: 0.0064 (0.0064) time: 2.2229 data: 0.9032 max mem: 6508 Epoch: [9] [ 10/104] eta: 0:01:48 lr: 0.001000 loss: 0.4245 (0.3695) loss_classifier: 0.1346 (0.1332) loss_box_reg: 0.2167 (0.2120) loss_objectness: 0.0120 (0.0134) loss_rpn_box_reg: 0.0103 (0.0109) time: 1.1574 data: 0.1001 max mem: 6508 Epoch: [9] [ 20/104] eta: 0:01:33 lr: 0.001000 loss: 0.4000 (0.3769) loss_classifier: 0.1377 (0.1363) loss_box_reg: 0.2237 (0.2162) loss_objectness: 0.0132 (0.0133) loss_rpn_box_reg: 0.0096 (0.0111) time: 1.0624 data: 0.0226 max mem: 6508 Epoch: [9] [ 30/104] eta: 0:01:21 lr: 0.001000 loss: 0.3995 (0.3815) loss_classifier: 0.1344 (0.1371) loss_box_reg: 0.2237 (0.2193) loss_objectness: 0.0121 (0.0129) loss_rpn_box_reg: 0.0085 (0.0122) time: 1.0685 data: 0.0258 max mem: 6508 Epoch: [9] [ 40/104] eta: 0:01:09 lr: 0.001000 loss: 0.4050 (0.3959) loss_classifier: 0.1295 (0.1445) loss_box_reg: 0.2357 (0.2252) loss_objectness: 0.0120 (0.0130) loss_rpn_box_reg: 0.0138 (0.0133) time: 1.0399 data: 0.0230 max mem: 6508 Epoch: [9] [ 50/104] eta: 0:00:57 lr: 0.001000 loss: 0.3894 (0.3892) loss_classifier: 0.1551 (0.1439) loss_box_reg: 0.2282 (0.2193) loss_objectness: 0.0118 (0.0129) loss_rpn_box_reg: 0.0138 (0.0130) time: 1.0168 data: 0.0216 max mem: 6508 Epoch: [9] [ 60/104] eta: 0:00:46 lr: 0.001000 loss: 0.3969 (0.3904) loss_classifier: 0.1575 (0.1450) loss_box_reg: 0.2105 (0.2176) loss_objectness: 0.0138 (0.0142) loss_rpn_box_reg: 0.0119 (0.0137) time: 1.0163 data: 0.0238 max mem: 6508 Epoch: [9] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.4003 (0.3942) loss_classifier: 0.1554 (0.1457) loss_box_reg: 0.2174 (0.2193) loss_objectness: 0.0166 (0.0145) loss_rpn_box_reg: 0.0149 (0.0147) time: 1.0169 data: 0.0235 max mem: 6508 Epoch: [9] [ 80/104] eta: 0:00:25 lr: 0.001000 loss: 0.3622 (0.3863) loss_classifier: 0.1343 (0.1428) loss_box_reg: 0.2125 (0.2151) loss_objectness: 0.0145 (0.0143) loss_rpn_box_reg: 0.0109 (0.0141) time: 1.0167 data: 0.0216 max mem: 6508 Epoch: [9] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.3177 (0.3828) loss_classifier: 0.1343 (0.1418) loss_box_reg: 0.1839 (0.2130) loss_objectness: 0.0109 (0.0142) loss_rpn_box_reg: 0.0097 (0.0137) time: 1.0233 data: 0.0214 max mem: 6508 Epoch: [9] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.3410 (0.3824) loss_classifier: 0.1387 (0.1423) loss_box_reg: 0.1886 (0.2123) loss_objectness: 0.0117 (0.0144) loss_rpn_box_reg: 0.0100 (0.0135) time: 1.0315 data: 0.0208 max mem: 6508 Epoch: [9] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.3467 (0.3798) loss_classifier: 0.1387 (0.1413) loss_box_reg: 0.1878 (0.2109) loss_objectness: 0.0140 (0.0144) loss_rpn_box_reg: 0.0100 (0.0133) time: 1.0285 data: 0.0197 max mem: 6508 Epoch: [9] Total time: 0:01:48 (1.0457 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:36 model_time: 0.4960 (0.4960) evaluator_time: 0.0317 (0.0317) time: 1.4057 data: 0.8676 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4394 (0.4397) evaluator_time: 0.0209 (0.0260) time: 0.4977 data: 0.0232 max mem: 6508 Test: Total time: 0:00:13 (0.5332 s / it) Averaged stats: model_time: 0.4394 (0.4397) evaluator_time: 0.0209 (0.0260) Accumulating evaluation results... DONE (t=0.15s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.361 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.709 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.307 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.323 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.422 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.232 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.159 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.449 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.509 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.447 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.322 Epoch: [10] [ 0/104] eta: 0:03:24 lr: 0.001000 loss: 0.3358 (0.3358) loss_classifier: 0.1290 (0.1290) loss_box_reg: 0.1879 (0.1879) loss_objectness: 0.0118 (0.0118) loss_rpn_box_reg: 0.0071 (0.0071) time: 1.9649 data: 0.8928 max mem: 6508 Epoch: [10] [ 10/104] eta: 0:01:47 lr: 0.001000 loss: 0.3452 (0.3785) loss_classifier: 0.1273 (0.1465) loss_box_reg: 0.1987 (0.2049) loss_objectness: 0.0131 (0.0130) loss_rpn_box_reg: 0.0083 (0.0141) time: 1.1438 data: 0.1010 max mem: 6508 Epoch: [10] [ 20/104] eta: 0:01:32 lr: 0.001000 loss: 0.3529 (0.3655) loss_classifier: 0.1237 (0.1324) loss_box_reg: 0.2196 (0.2063) loss_objectness: 0.0114 (0.0119) loss_rpn_box_reg: 0.0111 (0.0150) time: 1.0605 data: 0.0220 max mem: 6508 Epoch: [10] [ 30/104] eta: 0:01:20 lr: 0.001000 loss: 0.3529 (0.3614) loss_classifier: 0.1161 (0.1324) loss_box_reg: 0.2196 (0.2030) loss_objectness: 0.0101 (0.0123) loss_rpn_box_reg: 0.0108 (0.0136) time: 1.0472 data: 0.0211 max mem: 6508 Epoch: [10] [ 40/104] eta: 0:01:08 lr: 0.001000 loss: 0.3148 (0.3574) loss_classifier: 0.1141 (0.1295) loss_box_reg: 0.1836 (0.2014) loss_objectness: 0.0125 (0.0130) loss_rpn_box_reg: 0.0104 (0.0135) time: 1.0330 data: 0.0215 max mem: 6508 Epoch: [10] [ 50/104] eta: 0:00:57 lr: 0.001000 loss: 0.3396 (0.3581) loss_classifier: 0.1231 (0.1299) loss_box_reg: 0.2026 (0.2023) loss_objectness: 0.0125 (0.0129) loss_rpn_box_reg: 0.0113 (0.0131) time: 1.0262 data: 0.0221 max mem: 6508 Epoch: [10] [ 60/104] eta: 0:00:46 lr: 0.001000 loss: 0.3396 (0.3611) loss_classifier: 0.1231 (0.1306) loss_box_reg: 0.1967 (0.2043) loss_objectness: 0.0125 (0.0131) loss_rpn_box_reg: 0.0107 (0.0132) time: 1.0260 data: 0.0227 max mem: 6508 Epoch: [10] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.3545 (0.3665) loss_classifier: 0.1419 (0.1340) loss_box_reg: 0.2021 (0.2057) loss_objectness: 0.0160 (0.0137) loss_rpn_box_reg: 0.0101 (0.0131) time: 1.0250 data: 0.0231 max mem: 6508 Epoch: [10] [ 80/104] eta: 0:00:25 lr: 0.001000 loss: 0.3728 (0.3656) loss_classifier: 0.1448 (0.1329) loss_box_reg: 0.2101 (0.2054) loss_objectness: 0.0178 (0.0139) loss_rpn_box_reg: 0.0101 (0.0134) time: 1.0202 data: 0.0209 max mem: 6508 Epoch: [10] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.3848 (0.3708) loss_classifier: 0.1362 (0.1356) loss_box_reg: 0.2158 (0.2082) loss_objectness: 0.0120 (0.0138) loss_rpn_box_reg: 0.0096 (0.0132) time: 1.0240 data: 0.0202 max mem: 6508 Epoch: [10] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.4097 (0.3745) loss_classifier: 0.1414 (0.1367) loss_box_reg: 0.2242 (0.2109) loss_objectness: 0.0107 (0.0139) loss_rpn_box_reg: 0.0098 (0.0130) time: 1.0240 data: 0.0196 max mem: 6508 Epoch: [10] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.3852 (0.3717) loss_classifier: 0.1362 (0.1359) loss_box_reg: 0.2190 (0.2092) loss_objectness: 0.0107 (0.0138) loss_rpn_box_reg: 0.0092 (0.0129) time: 1.0259 data: 0.0195 max mem: 6508 Epoch: [10] Total time: 0:01:48 (1.0431 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:35 model_time: 0.5398 (0.5398) evaluator_time: 0.0720 (0.0720) time: 1.3554 data: 0.7296 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4400 (0.4421) evaluator_time: 0.0213 (0.0269) time: 0.4923 data: 0.0208 max mem: 6508 Test: Total time: 0:00:13 (0.5286 s / it) Averaged stats: model_time: 0.4400 (0.4421) evaluator_time: 0.0213 (0.0269) Accumulating evaluation results... DONE (t=0.15s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.366 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.724 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.323 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.310 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.153 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.447 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.509 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.455 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.289 Epoch: [11] [ 0/104] eta: 0:03:21 lr: 0.001000 loss: 0.3552 (0.3552) loss_classifier: 0.1059 (0.1059) loss_box_reg: 0.2213 (0.2213) loss_objectness: 0.0111 (0.0111) loss_rpn_box_reg: 0.0169 (0.0169) time: 1.9349 data: 0.8788 max mem: 6508 Epoch: [11] [ 10/104] eta: 0:01:46 lr: 0.001000 loss: 0.3552 (0.3607) loss_classifier: 0.1188 (0.1305) loss_box_reg: 0.2090 (0.2034) loss_objectness: 0.0128 (0.0137) loss_rpn_box_reg: 0.0129 (0.0130) time: 1.1327 data: 0.0965 max mem: 6508 Epoch: [11] [ 20/104] eta: 0:01:31 lr: 0.001000 loss: 0.3827 (0.3783) loss_classifier: 0.1198 (0.1367) loss_box_reg: 0.2090 (0.2148) loss_objectness: 0.0125 (0.0143) loss_rpn_box_reg: 0.0116 (0.0125) time: 1.0513 data: 0.0190 max mem: 6508 Epoch: [11] [ 30/104] eta: 0:01:19 lr: 0.001000 loss: 0.3661 (0.3711) loss_classifier: 0.1198 (0.1348) loss_box_reg: 0.2020 (0.2102) loss_objectness: 0.0122 (0.0132) loss_rpn_box_reg: 0.0103 (0.0128) time: 1.0449 data: 0.0216 max mem: 6508 Epoch: [11] [ 40/104] eta: 0:01:08 lr: 0.001000 loss: 0.2960 (0.3559) loss_classifier: 0.1169 (0.1307) loss_box_reg: 0.1678 (0.2006) loss_objectness: 0.0103 (0.0131) loss_rpn_box_reg: 0.0082 (0.0114) time: 1.0306 data: 0.0219 max mem: 6508 Epoch: [11] [ 50/104] eta: 0:00:56 lr: 0.001000 loss: 0.2937 (0.3545) loss_classifier: 0.1132 (0.1304) loss_box_reg: 0.1621 (0.1999) loss_objectness: 0.0102 (0.0129) loss_rpn_box_reg: 0.0082 (0.0113) time: 1.0160 data: 0.0204 max mem: 6508 Epoch: [11] [ 60/104] eta: 0:00:45 lr: 0.001000 loss: 0.3493 (0.3636) loss_classifier: 0.1264 (0.1330) loss_box_reg: 0.1996 (0.2051) loss_objectness: 0.0117 (0.0131) loss_rpn_box_reg: 0.0116 (0.0124) time: 1.0080 data: 0.0202 max mem: 6508 Epoch: [11] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.3441 (0.3586) loss_classifier: 0.1179 (0.1313) loss_box_reg: 0.1896 (0.2018) loss_objectness: 0.0141 (0.0135) loss_rpn_box_reg: 0.0105 (0.0119) time: 1.0125 data: 0.0210 max mem: 6508 Epoch: [11] [ 80/104] eta: 0:00:24 lr: 0.001000 loss: 0.3341 (0.3598) loss_classifier: 0.1228 (0.1318) loss_box_reg: 0.1810 (0.2028) loss_objectness: 0.0119 (0.0132) loss_rpn_box_reg: 0.0105 (0.0121) time: 1.0234 data: 0.0224 max mem: 6508 Epoch: [11] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.3917 (0.3616) loss_classifier: 0.1369 (0.1324) loss_box_reg: 0.2154 (0.2037) loss_objectness: 0.0131 (0.0133) loss_rpn_box_reg: 0.0124 (0.0123) time: 1.0307 data: 0.0232 max mem: 6508 Epoch: [11] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.3986 (0.3644) loss_classifier: 0.1348 (0.1332) loss_box_reg: 0.2154 (0.2056) loss_objectness: 0.0115 (0.0131) loss_rpn_box_reg: 0.0124 (0.0125) time: 1.0378 data: 0.0231 max mem: 6508 Epoch: [11] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.3917 (0.3665) loss_classifier: 0.1388 (0.1337) loss_box_reg: 0.2154 (0.2069) loss_objectness: 0.0113 (0.0131) loss_rpn_box_reg: 0.0134 (0.0128) time: 1.0384 data: 0.0229 max mem: 6508 Epoch: [11] Total time: 0:01:48 (1.0407 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:45 model_time: 0.5142 (0.5142) evaluator_time: 0.1822 (0.1822) time: 1.7422 data: 1.0378 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4337 (0.4395) evaluator_time: 0.0174 (0.0288) time: 0.4793 data: 0.0206 max mem: 6508 Test: Total time: 0:00:14 (0.5425 s / it) Averaged stats: model_time: 0.4337 (0.4395) evaluator_time: 0.0174 (0.0288) Accumulating evaluation results... DONE (t=0.15s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.732 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.336 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.301 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.429 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.254 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.161 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.451 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.513 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.445 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.550 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.339 Epoch: [12] [ 0/104] eta: 0:04:01 lr: 0.001000 loss: 0.3296 (0.3296) loss_classifier: 0.1243 (0.1243) loss_box_reg: 0.1716 (0.1716) loss_objectness: 0.0247 (0.0247) loss_rpn_box_reg: 0.0090 (0.0090) time: 2.3198 data: 0.9138 max mem: 6508 Epoch: [12] [ 10/104] eta: 0:01:49 lr: 0.001000 loss: 0.3754 (0.4018) loss_classifier: 0.1278 (0.1475) loss_box_reg: 0.2398 (0.2275) loss_objectness: 0.0135 (0.0140) loss_rpn_box_reg: 0.0115 (0.0128) time: 1.1628 data: 0.1006 max mem: 6508 Epoch: [12] [ 20/104] eta: 0:01:33 lr: 0.001000 loss: 0.3603 (0.3675) loss_classifier: 0.1220 (0.1319) loss_box_reg: 0.2210 (0.2105) loss_objectness: 0.0120 (0.0124) loss_rpn_box_reg: 0.0115 (0.0126) time: 1.0503 data: 0.0203 max mem: 6508 Epoch: [12] [ 30/104] eta: 0:01:20 lr: 0.001000 loss: 0.3440 (0.3694) loss_classifier: 0.1220 (0.1362) loss_box_reg: 0.1879 (0.2081) loss_objectness: 0.0107 (0.0127) loss_rpn_box_reg: 0.0107 (0.0124) time: 1.0448 data: 0.0215 max mem: 6508 Epoch: [12] [ 40/104] eta: 0:01:08 lr: 0.001000 loss: 0.3340 (0.3627) loss_classifier: 0.1333 (0.1333) loss_box_reg: 0.1853 (0.2040) loss_objectness: 0.0133 (0.0129) loss_rpn_box_reg: 0.0101 (0.0125) time: 1.0277 data: 0.0216 max mem: 6508 Epoch: [12] [ 50/104] eta: 0:00:57 lr: 0.001000 loss: 0.3332 (0.3669) loss_classifier: 0.1333 (0.1340) loss_box_reg: 0.1968 (0.2074) loss_objectness: 0.0113 (0.0127) loss_rpn_box_reg: 0.0101 (0.0129) time: 1.0174 data: 0.0218 max mem: 6508 Epoch: [12] [ 60/104] eta: 0:00:46 lr: 0.001000 loss: 0.3676 (0.3705) loss_classifier: 0.1290 (0.1345) loss_box_reg: 0.2198 (0.2096) loss_objectness: 0.0129 (0.0132) loss_rpn_box_reg: 0.0118 (0.0131) time: 1.0239 data: 0.0242 max mem: 6508 Epoch: [12] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.3456 (0.3656) loss_classifier: 0.1198 (0.1329) loss_box_reg: 0.1968 (0.2069) loss_objectness: 0.0132 (0.0132) loss_rpn_box_reg: 0.0099 (0.0126) time: 1.0260 data: 0.0231 max mem: 6508 Epoch: [12] [ 80/104] eta: 0:00:25 lr: 0.001000 loss: 0.3438 (0.3698) loss_classifier: 0.1352 (0.1343) loss_box_reg: 0.1954 (0.2089) loss_objectness: 0.0120 (0.0134) loss_rpn_box_reg: 0.0099 (0.0132) time: 1.0216 data: 0.0201 max mem: 6508 Epoch: [12] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.3364 (0.3691) loss_classifier: 0.1323 (0.1338) loss_box_reg: 0.1908 (0.2090) loss_objectness: 0.0100 (0.0131) loss_rpn_box_reg: 0.0101 (0.0132) time: 1.0283 data: 0.0202 max mem: 6508 Epoch: [12] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.3178 (0.3666) loss_classifier: 0.1175 (0.1327) loss_box_reg: 0.1908 (0.2086) loss_objectness: 0.0087 (0.0126) loss_rpn_box_reg: 0.0082 (0.0128) time: 1.0286 data: 0.0214 max mem: 6508 Epoch: [12] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.3118 (0.3633) loss_classifier: 0.1122 (0.1314) loss_box_reg: 0.1895 (0.2065) loss_objectness: 0.0087 (0.0127) loss_rpn_box_reg: 0.0085 (0.0127) time: 1.0235 data: 0.0212 max mem: 6508 Epoch: [12] Total time: 0:01:48 (1.0437 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:41 model_time: 0.5193 (0.5193) evaluator_time: 0.3265 (0.3265) time: 1.6055 data: 0.7408 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4387 (0.4419) evaluator_time: 0.0244 (0.0428) time: 0.5053 data: 0.0242 max mem: 6508 Test: Total time: 0:00:14 (0.5500 s / it) Averaged stats: model_time: 0.4387 (0.4419) evaluator_time: 0.0244 (0.0428) Accumulating evaluation results... DONE (t=0.15s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.372 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.738 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.323 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.306 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.427 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.267 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.159 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.454 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.513 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.441 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.368 Epoch: [13] [ 0/104] eta: 0:03:34 lr: 0.001000 loss: 0.3092 (0.3092) loss_classifier: 0.0881 (0.0881) loss_box_reg: 0.1915 (0.1915) loss_objectness: 0.0168 (0.0168) loss_rpn_box_reg: 0.0128 (0.0128) time: 2.0664 data: 1.0264 max mem: 6508 Epoch: [13] [ 10/104] eta: 0:01:46 lr: 0.001000 loss: 0.3466 (0.3489) loss_classifier: 0.1134 (0.1258) loss_box_reg: 0.1997 (0.1976) loss_objectness: 0.0141 (0.0151) loss_rpn_box_reg: 0.0102 (0.0104) time: 1.1362 data: 0.1088 max mem: 6508 Epoch: [13] [ 20/104] eta: 0:01:33 lr: 0.001000 loss: 0.3585 (0.3608) loss_classifier: 0.1336 (0.1343) loss_box_reg: 0.1997 (0.2015) loss_objectness: 0.0132 (0.0142) loss_rpn_box_reg: 0.0102 (0.0108) time: 1.0621 data: 0.0195 max mem: 6508 Epoch: [13] [ 30/104] eta: 0:01:20 lr: 0.001000 loss: 0.3632 (0.3590) loss_classifier: 0.1322 (0.1303) loss_box_reg: 0.2048 (0.2051) loss_objectness: 0.0106 (0.0130) loss_rpn_box_reg: 0.0103 (0.0106) time: 1.0673 data: 0.0204 max mem: 6508 Epoch: [13] [ 40/104] eta: 0:01:08 lr: 0.001000 loss: 0.3632 (0.3617) loss_classifier: 0.1311 (0.1330) loss_box_reg: 0.2053 (0.2040) loss_objectness: 0.0106 (0.0131) loss_rpn_box_reg: 0.0099 (0.0116) time: 1.0441 data: 0.0203 max mem: 6508 Epoch: [13] [ 50/104] eta: 0:00:57 lr: 0.001000 loss: 0.3369 (0.3605) loss_classifier: 0.1256 (0.1294) loss_box_reg: 0.1936 (0.2067) loss_objectness: 0.0104 (0.0126) loss_rpn_box_reg: 0.0092 (0.0118) time: 1.0233 data: 0.0217 max mem: 6508 Epoch: [13] [ 60/104] eta: 0:00:46 lr: 0.001000 loss: 0.3831 (0.3630) loss_classifier: 0.1317 (0.1311) loss_box_reg: 0.2342 (0.2079) loss_objectness: 0.0078 (0.0118) loss_rpn_box_reg: 0.0096 (0.0121) time: 1.0105 data: 0.0221 max mem: 6508 Epoch: [13] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.3831 (0.3632) loss_classifier: 0.1336 (0.1317) loss_box_reg: 0.2201 (0.2072) loss_objectness: 0.0090 (0.0120) loss_rpn_box_reg: 0.0122 (0.0124) time: 1.0019 data: 0.0209 max mem: 6508 Epoch: [13] [ 80/104] eta: 0:00:25 lr: 0.001000 loss: 0.3283 (0.3581) loss_classifier: 0.1210 (0.1308) loss_box_reg: 0.1896 (0.2034) loss_objectness: 0.0094 (0.0119) loss_rpn_box_reg: 0.0101 (0.0120) time: 1.0087 data: 0.0218 max mem: 6508 Epoch: [13] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.3066 (0.3566) loss_classifier: 0.1091 (0.1300) loss_box_reg: 0.1808 (0.2029) loss_objectness: 0.0094 (0.0117) loss_rpn_box_reg: 0.0087 (0.0120) time: 1.0290 data: 0.0224 max mem: 6508 Epoch: [13] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.3514 (0.3582) loss_classifier: 0.1135 (0.1301) loss_box_reg: 0.2078 (0.2040) loss_objectness: 0.0110 (0.0119) loss_rpn_box_reg: 0.0090 (0.0123) time: 1.0397 data: 0.0198 max mem: 6508 Epoch: [13] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.3514 (0.3583) loss_classifier: 0.1213 (0.1303) loss_box_reg: 0.2078 (0.2038) loss_objectness: 0.0110 (0.0118) loss_rpn_box_reg: 0.0119 (0.0124) time: 1.0423 data: 0.0195 max mem: 6508 Epoch: [13] Total time: 0:01:48 (1.0442 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:35 model_time: 0.5151 (0.5151) evaluator_time: 0.0298 (0.0298) time: 1.3750 data: 0.8028 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4341 (0.4373) evaluator_time: 0.0164 (0.0217) time: 0.4862 data: 0.0221 max mem: 6508 Test: Total time: 0:00:13 (0.5220 s / it) Averaged stats: model_time: 0.4341 (0.4373) evaluator_time: 0.0164 (0.0217) Accumulating evaluation results... DONE (t=0.15s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.380 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.727 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.353 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.298 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.424 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.257 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.166 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.455 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.517 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.450 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.552 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.362 Epoch: [14] [ 0/104] eta: 0:02:58 lr: 0.001000 loss: 0.4111 (0.4111) loss_classifier: 0.1705 (0.1705) loss_box_reg: 0.2171 (0.2171) loss_objectness: 0.0078 (0.0078) loss_rpn_box_reg: 0.0156 (0.0156) time: 1.7151 data: 0.5224 max mem: 6508 Epoch: [14] [ 10/104] eta: 0:01:43 lr: 0.001000 loss: 0.3929 (0.3500) loss_classifier: 0.1241 (0.1285) loss_box_reg: 0.2171 (0.1973) loss_objectness: 0.0107 (0.0108) loss_rpn_box_reg: 0.0111 (0.0133) time: 1.1013 data: 0.0654 max mem: 6508 Epoch: [14] [ 20/104] eta: 0:01:30 lr: 0.001000 loss: 0.3124 (0.3298) loss_classifier: 0.1204 (0.1192) loss_box_reg: 0.1759 (0.1870) loss_objectness: 0.0101 (0.0112) loss_rpn_box_reg: 0.0100 (0.0124) time: 1.0395 data: 0.0203 max mem: 6508 Epoch: [14] [ 30/104] eta: 0:01:18 lr: 0.001000 loss: 0.3136 (0.3314) loss_classifier: 0.1137 (0.1202) loss_box_reg: 0.1806 (0.1869) loss_objectness: 0.0101 (0.0113) loss_rpn_box_reg: 0.0090 (0.0130) time: 1.0452 data: 0.0235 max mem: 6508 Epoch: [14] [ 40/104] eta: 0:01:07 lr: 0.001000 loss: 0.3399 (0.3387) loss_classifier: 0.1130 (0.1212) loss_box_reg: 0.1890 (0.1926) loss_objectness: 0.0109 (0.0118) loss_rpn_box_reg: 0.0090 (0.0131) time: 1.0414 data: 0.0244 max mem: 6508 Epoch: [14] [ 50/104] eta: 0:00:56 lr: 0.001000 loss: 0.3399 (0.3483) loss_classifier: 0.1125 (0.1252) loss_box_reg: 0.2136 (0.1978) loss_objectness: 0.0117 (0.0120) loss_rpn_box_reg: 0.0099 (0.0133) time: 1.0269 data: 0.0220 max mem: 6508 Epoch: [14] [ 60/104] eta: 0:00:45 lr: 0.001000 loss: 0.3414 (0.3437) loss_classifier: 0.1187 (0.1243) loss_box_reg: 0.1988 (0.1951) loss_objectness: 0.0109 (0.0117) loss_rpn_box_reg: 0.0099 (0.0127) time: 1.0209 data: 0.0205 max mem: 6508 Epoch: [14] [ 70/104] eta: 0:00:35 lr: 0.001000 loss: 0.3355 (0.3447) loss_classifier: 0.1128 (0.1246) loss_box_reg: 0.1946 (0.1955) loss_objectness: 0.0098 (0.0121) loss_rpn_box_reg: 0.0096 (0.0124) time: 1.0244 data: 0.0207 max mem: 6508 Epoch: [14] [ 80/104] eta: 0:00:24 lr: 0.001000 loss: 0.3644 (0.3475) loss_classifier: 0.1185 (0.1261) loss_box_reg: 0.1995 (0.1973) loss_objectness: 0.0101 (0.0118) loss_rpn_box_reg: 0.0096 (0.0123) time: 1.0287 data: 0.0222 max mem: 6508 Epoch: [14] [ 90/104] eta: 0:00:14 lr: 0.001000 loss: 0.3639 (0.3469) loss_classifier: 0.1278 (0.1253) loss_box_reg: 0.2112 (0.1976) loss_objectness: 0.0102 (0.0118) loss_rpn_box_reg: 0.0093 (0.0122) time: 1.0274 data: 0.0234 max mem: 6508 Epoch: [14] [100/104] eta: 0:00:04 lr: 0.001000 loss: 0.3578 (0.3490) loss_classifier: 0.1257 (0.1258) loss_box_reg: 0.2131 (0.1993) loss_objectness: 0.0088 (0.0116) loss_rpn_box_reg: 0.0121 (0.0123) time: 1.0202 data: 0.0212 max mem: 6508 Epoch: [14] [103/104] eta: 0:00:01 lr: 0.001000 loss: 0.3658 (0.3506) loss_classifier: 0.1278 (0.1266) loss_box_reg: 0.2145 (0.2002) loss_objectness: 0.0079 (0.0114) loss_rpn_box_reg: 0.0115 (0.0123) time: 1.0212 data: 0.0212 max mem: 6508 Epoch: [14] Total time: 0:01:48 (1.0386 s / it) creating index... index created! Test: [ 0/26] eta: 0:00:50 model_time: 0.6277 (0.6277) evaluator_time: 0.1529 (0.1529) time: 1.9418 data: 1.1288 max mem: 6508 Test: [25/26] eta: 0:00:00 model_time: 0.4307 (0.4378) evaluator_time: 0.0172 (0.0249) time: 0.4760 data: 0.0193 max mem: 6508 Test: Total time: 0:00:14 (0.5392 s / it) Averaged stats: model_time: 0.4307 (0.4378) evaluator_time: 0.0172 (0.0249) Accumulating evaluation results... DONE (t=0.23s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.391 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.762 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.363 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.314 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.436 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.260 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.165 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.464 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.529 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.463 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.569 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.375
import re
# Define dictionaries to hold your data
metrics = {
"AR_1": [],
"AR_10": [],
"AR_100": [],
"AR_small": [],
"AR_medium": [],
"AR_large": [],
"AP": [], # Add AP metric
"AP_50": [], # Add AP_50 metric
"AP_75": [], # Add AP_75 metric
"loss": [], # Add loss metric
"loss_classifier": [], # Add loss_classifier metric
"loss_box_reg": [], # Add loss_box_reg metric
"loss_objectness": [], # Add loss_objectness metric
"loss_rpn_box_reg": [], # Add loss_rpn_box_reg metric
"model_time": [], # Add model_time metric
"evaluator_time": [], # Add evaluator_time metric
"total_time": [] # Add total_time metric
}
# Regex patterns to extract data
pattern_ar_1 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 1 \] = (\d+\.\d+)")
pattern_ar_10 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 10 \] = (\d+\.\d+)")
pattern_ar_100 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_small = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= small \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_medium = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area=medium \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_large = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= large \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_50 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_75 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.75 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_loss = re.compile(r"loss: (\d+\.\d+)") # Pattern for loss
pattern_loss_classifier = re.compile(r"loss_classifier: (\d+\.\d+)") # Pattern for loss_classifier
pattern_loss_box_reg = re.compile(r"loss_box_reg: (\d+\.\d+)") # Pattern for loss_box_reg
pattern_loss_objectness = re.compile(r"loss_objectness: (\d+\.\d+)") # Pattern for loss_objectness
pattern_loss_rpn_box_reg = re.compile(r"loss_rpn_box_reg: (\d+\.\d+)")# Pattern for loss_rpn_box_reg
pattern_model_time = re.compile(r"Model time:\s+(\d+\.\d+)") # Pattern for model_time
pattern_evaluator_time = re.compile(r"Evaluator time:\s+(\d+\.\d+)")# Pattern for evaluator_time
pattern_total_time = re.compile(r"Total time:\s+(\d+\.\d+)") # Pattern for total_time
# Read the log file
with open('eva adamn_sgd.txt', 'r') as file:
file_content = file.read()
# Handling AR matches
metrics["AR_1"].extend([float(x) for x in pattern_ar_1.findall(file_content)])
metrics["AR_10"].extend([float(x) for x in pattern_ar_10.findall(file_content)])
metrics["AR_100"].extend([float(x) for x in pattern_ar_100.findall(file_content)])
metrics["AR_small"].extend([float(x) for x in pattern_ar_small.findall(file_content)])
metrics["AR_medium"].extend([float(x) for x in pattern_ar_medium.findall(file_content)])
metrics["AR_large"].extend([float(x) for x in pattern_ar_large.findall(file_content)])
# Handling AP matches
metrics["AP"].extend([float(x) for x in pattern_ap.findall(file_content)])
metrics["AP_50"].extend([float(x) for x in pattern_ap_50.findall(file_content)])
metrics["AP_75"].extend([float(x) for x in pattern_ap_75.findall(file_content)])
# Handling loss matches
metrics["loss"].extend([float(x) for x in pattern_loss.findall(file_content)])
# Handling loss_classifier matches
metrics["loss_classifier"].extend([float(x) for x in pattern_loss_classifier.findall(file_content)])
# Handling loss_box_reg matches
metrics["loss_box_reg"].extend([float(x) for x in pattern_loss_box_reg.findall(file_content)])
# Handling loss_objectness matches
metrics["loss_objectness"].extend([float(x) for x in pattern_loss_objectness.findall(file_content)])
# Handling loss_rpn_box_reg matches
metrics["loss_rpn_box_reg"].extend([float(x) for x in pattern_loss_rpn_box_reg.findall(file_content)])
# Handling model_time matches
metrics["model_time"].extend([float(x) for x in pattern_model_time.findall(file_content)])
# Handling evaluator_time matches
metrics["evaluator_time"].extend([float(x) for x in pattern_evaluator_time.findall(file_content)])
# Handling total_time matches
metrics["total_time"].extend([float(x) for x in pattern_total_time.findall(file_content)])
# Print the collected metrics to verify
for key, value in metrics.items():
print(f"{key}: {value}")
AR_1: [0.073, 0.007, 0.045, 0.071, 0.105, 0.123, 0.153, 0.157, 0.154, 0.159, 0.153, 0.161, 0.159, 0.166, 0.165] AR_10: [0.178, 0.056, 0.174, 0.259, 0.315, 0.375, 0.433, 0.442, 0.438, 0.449, 0.447, 0.451, 0.454, 0.455, 0.464] AR_100: [0.223, 0.101, 0.226, 0.323, 0.371, 0.441, 0.501, 0.506, 0.494, 0.509, 0.509, 0.513, 0.513, 0.517, 0.529] AR_small: [0.246, 0.115, 0.235, 0.334, 0.35, 0.376, 0.413, 0.434, 0.441, 0.447, 0.455, 0.445, 0.441, 0.45, 0.463] AR_medium: [0.178, 0.154, 0.278, 0.374, 0.373, 0.504, 0.553, 0.532, 0.533, 0.544, 0.544, 0.55, 0.547, 0.552, 0.569] AR_large: [0.221, 0.0, 0.09, 0.06, 0.159, 0.237, 0.329, 0.317, 0.307, 0.322, 0.289, 0.339, 0.368, 0.362, 0.375] AP: [0.098, 0.032, 0.095, 0.173, 0.225, 0.29, 0.347, 0.355, 0.353, 0.361, 0.366, 0.371, 0.372, 0.38, 0.391] AP_50: [] AP_75: [] loss: [3.3583, 2.3279, 1.209, 0.9238, 0.879, 0.941, 0.8082, 0.6632, 0.5934, 0.8766, 0.7816, 0.6924, 0.5595, 0.9238, 0.9238, 0.8937, 0.8828, 0.8035, 0.7846, 0.7665, 0.7328, 0.8004, 0.8303, 0.8303, 0.5037, 0.7679, 0.7679, 0.6997, 0.6524, 0.6844, 0.6844, 0.7528, 0.8539, 0.8539, 0.6873, 0.6611, 0.9499, 0.8302, 0.6848, 0.7425, 0.7538, 0.6383, 0.6383, 0.5951, 0.6758, 0.6085, 0.5904, 0.6085, 0.9558, 0.7101, 0.6309, 0.6323, 0.6639, 0.575, 0.633, 0.6562, 0.6114, 0.5727, 0.5472, 0.5416, 0.468, 0.4746, 0.5318, 0.6038, 0.581, 0.5266, 0.5092, 0.4859, 0.5286, 0.5525, 0.5126, 0.4816, 0.2004, 0.379, 0.379, 0.3963, 0.4557, 0.4646, 0.4693, 0.4249, 0.4432, 0.4432, 0.3908, 0.3908, 0.5793, 0.3872, 0.3806, 0.4133, 0.3974, 0.35, 0.36, 0.3652, 0.3829, 0.4413, 0.4413, 0.4344, 0.2473, 0.3371, 0.3752, 0.3752, 0.371, 0.371, 0.3706, 0.3706, 0.3642, 0.3939, 0.3779, 0.3779, 0.2582, 0.4245, 0.4, 0.3995, 0.405, 0.3894, 0.3969, 0.4003, 0.3622, 0.3177, 0.341, 0.3467, 0.3358, 0.3452, 0.3529, 0.3529, 0.3148, 0.3396, 0.3396, 0.3545, 0.3728, 0.3848, 0.4097, 0.3852, 0.3552, 0.3552, 0.3827, 0.3661, 0.296, 0.2937, 0.3493, 0.3441, 0.3341, 0.3917, 0.3986, 0.3917, 0.3296, 0.3754, 0.3603, 0.344, 0.334, 0.3332, 0.3676, 0.3456, 0.3438, 0.3364, 0.3178, 0.3118, 0.3092, 0.3466, 0.3585, 0.3632, 0.3632, 0.3369, 0.3831, 0.3831, 0.3283, 0.3066, 0.3514, 0.3514, 0.4111, 0.3929, 0.3124, 0.3136, 0.3399, 0.3399, 0.3414, 0.3355, 0.3644, 0.3639, 0.3578, 0.3658] loss_classifier: [2.5736, 1.5769, 0.664, 0.4708, 0.4517, 0.4517, 0.3806, 0.3152, 0.3131, 0.3554, 0.3472, 0.3055, 0.2305, 0.3809, 0.4395, 0.4452, 0.4144, 0.3222, 0.3764, 0.3365, 0.3365, 0.3582, 0.3423, 0.3094, 0.2505, 0.263, 0.3391, 0.3233, 0.3024, 0.3101, 0.3198, 0.3344, 0.3837, 0.3838, 0.2968, 0.2968, 0.3417, 0.3417, 0.3111, 0.3458, 0.282, 0.2502, 0.2553, 0.2605, 0.2742, 0.2477, 0.2427, 0.2726, 0.493, 0.2657, 0.2392, 0.2409, 0.275, 0.2283, 0.2227, 0.2589, 0.2353, 0.2379, 0.2376, 0.2112, 0.1478, 0.2071, 0.2175, 0.235, 0.235, 0.2094, 0.1943, 0.1813, 0.1831, 0.1898, 0.1921, 0.1829, 0.085, 0.1573, 0.1573, 0.1639, 0.1723, 0.1774, 0.1746, 0.1655, 0.1613, 0.1654, 0.1517, 0.1499, 0.2718, 0.1535, 0.142, 0.1405, 0.1418, 0.1315, 0.1315, 0.1331, 0.1563, 0.1581, 0.159, 0.152, 0.0932, 0.1366, 0.1365, 0.1365, 0.1305, 0.1323, 0.1415, 0.1418, 0.1418, 0.1465, 0.1397, 0.1397, 0.0994, 0.1346, 0.1377, 0.1344, 0.1295, 0.1551, 0.1575, 0.1554, 0.1343, 0.1343, 0.1387, 0.1387, 0.129, 0.1273, 0.1237, 0.1161, 0.1141, 0.1231, 0.1231, 0.1419, 0.1448, 0.1362, 0.1414, 0.1362, 0.1059, 0.1188, 0.1198, 0.1198, 0.1169, 0.1132, 0.1264, 0.1179, 0.1228, 0.1369, 0.1348, 0.1388, 0.1243, 0.1278, 0.122, 0.122, 0.1333, 0.1333, 0.129, 0.1198, 0.1352, 0.1323, 0.1175, 0.1122, 0.0881, 0.1134, 0.1336, 0.1322, 0.1311, 0.1256, 0.1317, 0.1336, 0.121, 0.1091, 0.1135, 0.1213, 0.1705, 0.1241, 0.1204, 0.1137, 0.113, 0.1125, 0.1187, 0.1128, 0.1185, 0.1278, 0.1257, 0.1278] loss_box_reg: [0.4333, 0.359, 0.359, 0.3461, 0.3577, 0.3279, 0.3105, 0.2753, 0.2408, 0.3787, 0.3829, 0.3446, 0.2976, 0.42, 0.3396, 0.257, 0.2087, 0.1765, 0.2107, 0.178, 0.207, 0.2739, 0.2363, 0.2845, 0.1813, 0.3321, 0.3321, 0.2844, 0.238, 0.2446, 0.2572, 0.3064, 0.3581, 0.3581, 0.2919, 0.2749, 0.4621, 0.3616, 0.3178, 0.3276, 0.3276, 0.3137, 0.2984, 0.2664, 0.3144, 0.3144, 0.2899, 0.31, 0.3486, 0.3435, 0.293, 0.297, 0.297, 0.2813, 0.2909, 0.3541, 0.2933, 0.2812, 0.2812, 0.2812, 0.2861, 0.2416, 0.2739, 0.289, 0.2642, 0.2642, 0.2546, 0.2525, 0.2902, 0.3005, 0.2651, 0.2651, 0.0903, 0.1895, 0.2113, 0.2222, 0.2258, 0.2445, 0.2554, 0.2321, 0.2495, 0.2633, 0.2001, 0.2096, 0.2628, 0.2187, 0.2103, 0.2212, 0.1994, 0.1885, 0.208, 0.2129, 0.2107, 0.2467, 0.2542, 0.2392, 0.1216, 0.1766, 0.1974, 0.2118, 0.2202, 0.2105, 0.1955, 0.1955, 0.2009, 0.2182, 0.2145, 0.2145, 0.1455, 0.2167, 0.2237, 0.2237, 0.2357, 0.2282, 0.2105, 0.2174, 0.2125, 0.1839, 0.1886, 0.1878, 0.1879, 0.1987, 0.2196, 0.2196, 0.1836, 0.2026, 0.1967, 0.2021, 0.2101, 0.2158, 0.2242, 0.219, 0.2213, 0.209, 0.209, 0.202, 0.1678, 0.1621, 0.1996, 0.1896, 0.181, 0.2154, 0.2154, 0.2154, 0.1716, 0.2398, 0.221, 0.1879, 0.1853, 0.1968, 0.2198, 0.1968, 0.1954, 0.1908, 0.1908, 0.1895, 0.1915, 0.1997, 0.1997, 0.2048, 0.2053, 0.1936, 0.2342, 0.2201, 0.1896, 0.1808, 0.2078, 0.2078, 0.2171, 0.2171, 0.1759, 0.1806, 0.189, 0.2136, 0.1988, 0.1946, 0.1995, 0.2112, 0.2131, 0.2145] loss_objectness: [0.3133, 0.1724, 0.0846, 0.0666, 0.0476, 0.0535, 0.0697, 0.0606, 0.0489, 0.0441, 0.043, 0.0425, 0.0189, 0.0522, 0.0792, 0.16, 0.178, 0.1685, 0.1683, 0.1311, 0.1122, 0.1272, 0.1451, 0.1452, 0.0628, 0.1093, 0.1086, 0.1138, 0.0968, 0.0753, 0.0772, 0.0596, 0.0577, 0.0537, 0.0533, 0.0537, 0.1011, 0.0448, 0.0435, 0.0473, 0.041, 0.041, 0.044, 0.0458, 0.0472, 0.0452, 0.0452, 0.041, 0.0946, 0.0472, 0.0401, 0.0392, 0.037, 0.0317, 0.0316, 0.0412, 0.0382, 0.037, 0.0378, 0.0353, 0.0217, 0.025, 0.0252, 0.0325, 0.0323, 0.031, 0.0306, 0.0248, 0.0236, 0.0242, 0.0218, 0.0218, 0.0186, 0.0203, 0.022, 0.023, 0.0183, 0.0139, 0.0151, 0.0179, 0.015, 0.0155, 0.0155, 0.0147, 0.0261, 0.0147, 0.0147, 0.0172, 0.018, 0.0163, 0.0157, 0.0148, 0.0132, 0.0141, 0.0126, 0.0126, 0.0231, 0.0176, 0.0141, 0.0131, 0.0123, 0.0123, 0.011, 0.011, 0.0159, 0.0168, 0.0126, 0.0129, 0.0069, 0.012, 0.0132, 0.0121, 0.012, 0.0118, 0.0138, 0.0166, 0.0145, 0.0109, 0.0117, 0.014, 0.0118, 0.0131, 0.0114, 0.0101, 0.0125, 0.0125, 0.0125, 0.016, 0.0178, 0.012, 0.0107, 0.0107, 0.0111, 0.0128, 0.0125, 0.0122, 0.0103, 0.0102, 0.0117, 0.0141, 0.0119, 0.0131, 0.0115, 0.0113, 0.0247, 0.0135, 0.012, 0.0107, 0.0133, 0.0113, 0.0129, 0.0132, 0.012, 0.01, 0.0087, 0.0087, 0.0168, 0.0141, 0.0132, 0.0106, 0.0106, 0.0104, 0.0078, 0.009, 0.0094, 0.0094, 0.011, 0.011, 0.0078, 0.0107, 0.0101, 0.0101, 0.0109, 0.0117, 0.0109, 0.0098, 0.0101, 0.0102, 0.0088, 0.0079] loss_rpn_box_reg: [0.0381, 0.02, 0.0191, 0.0139, 0.014, 0.0218, 0.0244, 0.0175, 0.0122, 0.0196, 0.02, 0.0163, 0.0124, 0.0273, 0.0273, 0.0256, 0.0408, 0.0305, 0.0292, 0.0338, 0.021, 0.0222, 0.0222, 0.0225, 0.0091, 0.0268, 0.028, 0.0254, 0.0232, 0.0267, 0.024, 0.0235, 0.0265, 0.0274, 0.0234, 0.018, 0.045, 0.0273, 0.0234, 0.0217, 0.0217, 0.0182, 0.0172, 0.0172, 0.0216, 0.0232, 0.0188, 0.0193, 0.0195, 0.0195, 0.021, 0.021, 0.0238, 0.0209, 0.0194, 0.0197, 0.0213, 0.0217, 0.0183, 0.0182, 0.0124, 0.0146, 0.0154, 0.0162, 0.014, 0.0175, 0.0192, 0.0164, 0.0197, 0.0217, 0.017, 0.017, 0.0066, 0.0126, 0.0126, 0.0141, 0.015, 0.0146, 0.0173, 0.0166, 0.013, 0.013, 0.0096, 0.0106, 0.0186, 0.0158, 0.0118, 0.0121, 0.0115, 0.011, 0.0108, 0.0103, 0.0103, 0.0127, 0.0147, 0.0147, 0.0095, 0.0103, 0.0125, 0.0138, 0.0119, 0.0106, 0.0138, 0.0138, 0.0133, 0.0131, 0.0105, 0.0105, 0.0064, 0.0103, 0.0096, 0.0085, 0.0138, 0.0138, 0.0119, 0.0149, 0.0109, 0.0097, 0.01, 0.01, 0.0071, 0.0083, 0.0111, 0.0108, 0.0104, 0.0113, 0.0107, 0.0101, 0.0101, 0.0096, 0.0098, 0.0092, 0.0169, 0.0129, 0.0116, 0.0103, 0.0082, 0.0082, 0.0116, 0.0105, 0.0105, 0.0124, 0.0124, 0.0134, 0.009, 0.0115, 0.0115, 0.0107, 0.0101, 0.0101, 0.0118, 0.0099, 0.0099, 0.0101, 0.0082, 0.0085, 0.0128, 0.0102, 0.0102, 0.0103, 0.0099, 0.0092, 0.0096, 0.0122, 0.0101, 0.0087, 0.009, 0.0119, 0.0156, 0.0111, 0.01, 0.009, 0.009, 0.0099, 0.0099, 0.0096, 0.0096, 0.0093, 0.0121, 0.0115] model_time: [] evaluator_time: [] total_time: []
import pickle
Filename = "FRCNNsgd.pkl"
# Define the file path where you want to save the model
filename = "/content/drive/MyDrive/dataset1/FRCNN1.pth"
# Save the model to the specified file path
torch.save(model.state_dict(), filename)
# Save the Modle to file in the current working directory
with open(Filename, 'wb') as file:
pickle.dump(model, file)
# Load the Model back from file
with open(Filename, 'rb') as file:
model = pickle.load(file)
model
FasterRCNN(
(transform): GeneralizedRCNNTransform(
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
Resize(min_size=(800,), max_size=1333, mode='bilinear')
)
(backbone): BackboneWithFPN(
(body): IntermediateLayerGetter(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): FrozenBatchNorm2d(256, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(512, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(1024, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(2048, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
)
)
(fpn): FeaturePyramidNetwork(
(inner_blocks): ModuleList(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
)
(1): Conv2dNormActivation(
(0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
(2): Conv2dNormActivation(
(0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
)
(3): Conv2dNormActivation(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
)
)
(layer_blocks): ModuleList(
(0-3): 4 x Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(extra_blocks): LastLevelMaxPool()
)
)
(rpn): RegionProposalNetwork(
(anchor_generator): AnchorGenerator()
(head): RPNHead(
(conv): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
)
)
(cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
(bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
)
)
(roi_heads): RoIHeads(
(box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)
(box_head): TwoMLPHead(
(fc6): Linear(in_features=12544, out_features=1024, bias=True)
(fc7): Linear(in_features=1024, out_features=1024, bias=True)
)
(box_predictor): FastRCNNPredictor(
(cls_score): Linear(in_features=1024, out_features=11, bias=True)
(bbox_pred): Linear(in_features=1024, out_features=44, bias=True)
)
)
)
import matplotlib.pyplot as plt
# Number of epochs or iterations
epochs = list(range(1, len(metrics["AP"]) + 1))
# Create a figure and axis for plotting
plt.figure(figsize=(10, 6))
# Plotting precision metrics
plt.plot(epochs, metrics["AP"], label='AP [IoU=0.50:0.95]', marker='o')
# Plotting recall metrics
plt.plot(epochs, metrics["AR_1"], label='AR [maxDets=1]', marker='o')
plt.plot(epochs, metrics["AR_10"], label='AR [maxDets=10]', marker='o')
plt.plot(epochs, metrics["AR_100"], label='AR [maxDets=100]', marker='o')
# Adding titles and labels
plt.title('AP and AR Metrics Over Epochs')
plt.xlabel('Epoch')
plt.ylabel('Metric Value')
plt.legend()
# Show the plot
plt.show()
import matplotlib.pyplot as plt
# Number of epochs or iterations
epochs = list(range(1, len(metrics["loss"]) + 1))
# Create a figure and axis for plotting
plt.figure(figsize=(10, 6))
# Plotting all loss metrics
plt.plot(epochs, metrics["loss"], label='Total Loss', marker='o')
plt.plot(epochs, metrics["loss_classifier"], label='Classifier Loss', marker='o')
plt.plot(epochs, metrics["loss_box_reg"], label='Box Reg. Loss', marker='o')
plt.plot(epochs, metrics["loss_objectness"], label='Objectness Loss', marker='o')
plt.plot(epochs, metrics["loss_rpn_box_reg"], label='RPN Box Reg. Loss', marker='o')
# Adding titles and labels
plt.title('Loss Metrics Over Epochs')
plt.xlabel('Epoch')
plt.ylabel('Loss Value')
plt.legend()
# Show the plot
plt.show()
import re
# Define dictionaries to hold your data
metrics = {
"AR_1": [],
"AR_10": [],
"AR_100": [],
"AR_small": [],
"AR_medium": [],
"AR_large": [],
"AP": [], # Add AP metric
"AP_50": [], # Add AP_50 metric
"AP_75": [], # Add AP_75 metric
"loss": [], # Add loss metric
"loss_classifier": [], # Add loss_classifier metric
"loss_box_reg": [], # Add loss_box_reg metric
"loss_objectness": [], # Add loss_objectness metric
"loss_rpn_box_reg": [], # Add loss_rpn_box_reg metric
"model_time": [], # Add model_time metric
"evaluator_time": [], # Add evaluator_time metric
"total_time": [] # Add total_time metric
}
# Regex patterns to extract data
pattern_ar_1 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 1 \] = (\d+\.\d+)")
pattern_ar_10 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 10 \] = (\d+\.\d+)")
pattern_ar_100 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_small = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= small \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_medium = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area=medium \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_large = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= large \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_50 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_75 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.75 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_loss = re.compile(r"loss: (\d+\.\d+)") # Pattern for loss
pattern_loss_classifier = re.compile(r"loss_classifier: (\d+\.\d+)") # Pattern for loss_classifier
pattern_loss_box_reg = re.compile(r"loss_box_reg: (\d+\.\d+)") # Pattern for loss_box_reg
pattern_loss_objectness = re.compile(r"loss_objectness: (\d+\.\d+)") # Pattern for loss_objectness
pattern_loss_rpn_box_reg = re.compile(r"loss_rpn_box_reg: (\d+\.\d+)")# Pattern for loss_rpn_box_reg
pattern_model_time = re.compile(r"Model time:\s+(\d+\.\d+)") # Pattern for model_time
pattern_evaluator_time = re.compile(r"Evaluator time:\s+(\d+\.\d+)")# Pattern for evaluator_time
pattern_total_time = re.compile(r"Total time:\s+(\d+\.\d+)") # Pattern for total_time
# Read the log file
with open('eva sgd.txt', 'r') as file:
file_content = file.read()
# Handling AR matches
metrics["AR_1"].extend([float(x) for x in pattern_ar_1.findall(file_content)])
metrics["AR_10"].extend([float(x) for x in pattern_ar_10.findall(file_content)])
metrics["AR_100"].extend([float(x) for x in pattern_ar_100.findall(file_content)])
metrics["AR_small"].extend([float(x) for x in pattern_ar_small.findall(file_content)])
metrics["AR_medium"].extend([float(x) for x in pattern_ar_medium.findall(file_content)])
metrics["AR_large"].extend([float(x) for x in pattern_ar_large.findall(file_content)])
# Handling AP matches
metrics["AP"].extend([float(x) for x in pattern_ap.findall(file_content)])
metrics["AP_50"].extend([float(x) for x in pattern_ap_50.findall(file_content)])
metrics["AP_75"].extend([float(x) for x in pattern_ap_75.findall(file_content)])
# Handling loss matches
metrics["loss"].extend([float(x) for x in pattern_loss.findall(file_content)])
# Handling loss_classifier matches
metrics["loss_classifier"].extend([float(x) for x in pattern_loss_classifier.findall(file_content)])
# Handling loss_box_reg matches
metrics["loss_box_reg"].extend([float(x) for x in pattern_loss_box_reg.findall(file_content)])
# Handling loss_objectness matches
metrics["loss_objectness"].extend([float(x) for x in pattern_loss_objectness.findall(file_content)])
# Handling loss_rpn_box_reg matches
metrics["loss_rpn_box_reg"].extend([float(x) for x in pattern_loss_rpn_box_reg.findall(file_content)])
# Handling model_time matches
metrics["model_time"].extend([float(x) for x in pattern_model_time.findall(file_content)])
# Handling evaluator_time matches
metrics["evaluator_time"].extend([float(x) for x in pattern_evaluator_time.findall(file_content)])
# Handling total_time matches
metrics["total_time"].extend([float(x) for x in pattern_total_time.findall(file_content)])
# Print the collected metrics to verify
for key, value in metrics.items():
print(f"{key}: {value}")
AR_1: [0.146, 0.173, 0.196, 0.229, 0.228, 0.234, 0.236, 0.234, 0.235, 0.235, 0.236, 0.236, 0.236, 0.236, 0.236] AR_10: [0.359, 0.444, 0.471, 0.537, 0.54, 0.544, 0.544, 0.543, 0.543, 0.543, 0.544, 0.544, 0.544, 0.544, 0.544] AR_100: [0.426, 0.511, 0.528, 0.614, 0.615, 0.61, 0.613, 0.613, 0.614, 0.615, 0.615, 0.615, 0.615, 0.615, 0.615] AR_small: [0.478, 0.442, 0.49, 0.556, 0.561, 0.541, 0.546, 0.549, 0.547, 0.547, 0.548, 0.548, 0.548, 0.548, 0.548] AR_medium: [0.405, 0.524, 0.513, 0.596, 0.602, 0.597, 0.599, 0.599, 0.601, 0.601, 0.602, 0.602, 0.602, 0.602, 0.602] AR_large: [0.515, 0.518, 0.582, 0.663, 0.673, 0.678, 0.685, 0.673, 0.673, 0.673, 0.673, 0.673, 0.673, 0.673, 0.673] AP: [0.298, 0.404, 0.427, 0.534, 0.54, 0.543, 0.548, 0.547, 0.548, 0.548, 0.548, 0.549, 0.549, 0.549, 0.549] AP_50: [] AP_75: [] loss: [2.501, 2.1539, 1.2824, 1.1755, 0.9232, 0.903, 0.903, 0.8739, 0.7396, 0.7196, 0.6679, 0.6873, 0.9491, 0.6119, 0.5908, 0.5204, 0.4952, 0.4952, 0.4953, 0.5289, 0.512, 0.5059, 0.4005, 0.3574, 0.2529, 0.3099, 0.3128, 0.3433, 0.3896, 0.3336, 0.2878, 0.3079, 0.3821, 0.402, 0.3498, 0.3488, 0.3512, 0.3512, 0.3133, 0.2674, 0.2504, 0.2161, 0.2449, 0.2423, 0.2168, 0.2075, 0.2281, 0.2375, 0.2486, 0.2155, 0.1932, 0.2339, 0.213, 0.221, 0.2511, 0.236, 0.2556, 0.2548, 0.2291, 0.2299, 0.3329, 0.236, 0.2198, 0.2059, 0.2017, 0.2017, 0.2068, 0.2366, 0.2242, 0.2099, 0.231, 0.239, 0.1748, 0.245, 0.2194, 0.2048, 0.1756, 0.1981, 0.2118, 0.2118, 0.2146, 0.2061, 0.2095, 0.2186, 0.3377, 0.2396, 0.1979, 0.1922, 0.216, 0.212, 0.1899, 0.1954, 0.2007, 0.1722, 0.2126, 0.2127, 0.1904, 0.1936, 0.1984, 0.2162, 0.2084, 0.2009, 0.215, 0.215, 0.2228, 0.2226, 0.1706, 0.1645, 0.1976, 0.1904, 0.1882, 0.1947, 0.1974, 0.1927, 0.2107, 0.2091, 0.2091, 0.2247, 0.2146, 0.211, 0.2751, 0.228, 0.1922, 0.2016, 0.1926, 0.1722, 0.199, 0.2036, 0.2014, 0.2014, 0.1887, 0.1882, 0.1635, 0.199, 0.199, 0.2375, 0.2013, 0.167, 0.167, 0.1836, 0.2026, 0.2026, 0.1901, 0.2141, 0.143, 0.2299, 0.2113, 0.177, 0.1848, 0.1868, 0.2124, 0.2013, 0.1894, 0.2155, 0.1985, 0.1981, 0.2508, 0.1942, 0.2218, 0.2237, 0.2167, 0.2058, 0.18, 0.1557, 0.165, 0.1724, 0.2136, 0.2011, 0.1479, 0.2075, 0.1953, 0.1721, 0.2, 0.2065, 0.1814, 0.1878, 0.19, 0.1885, 0.1979, 0.2031] loss_classifier: [2.0748, 1.6329, 0.7298, 0.6021, 0.4607, 0.4386, 0.4437, 0.4131, 0.3503, 0.3234, 0.2767, 0.2767, 0.4312, 0.203, 0.203, 0.1771, 0.1601, 0.1601, 0.1667, 0.1667, 0.1708, 0.1356, 0.1111, 0.1061, 0.0738, 0.0912, 0.0937, 0.0937, 0.094, 0.076, 0.0715, 0.089, 0.1129, 0.1104, 0.0948, 0.0948, 0.0984, 0.0903, 0.0721, 0.0729, 0.0707, 0.0646, 0.0796, 0.0679, 0.0586, 0.0661, 0.0661, 0.0669, 0.0577, 0.0577, 0.0612, 0.0727, 0.0633, 0.0633, 0.0637, 0.0629, 0.0711, 0.0719, 0.0654, 0.0654, 0.0899, 0.0729, 0.0586, 0.0529, 0.0542, 0.0527, 0.0559, 0.065, 0.0663, 0.0635, 0.0614, 0.0644, 0.0497, 0.0689, 0.0646, 0.0552, 0.0488, 0.0522, 0.0613, 0.0564, 0.0564, 0.0595, 0.0598, 0.0608, 0.0893, 0.0663, 0.0567, 0.0567, 0.0579, 0.0539, 0.0537, 0.0578, 0.057, 0.0577, 0.0583, 0.0603, 0.0511, 0.0489, 0.0493, 0.058, 0.0619, 0.0622, 0.0575, 0.0575, 0.061, 0.0574, 0.0499, 0.0468, 0.0576, 0.0542, 0.0529, 0.0508, 0.0534, 0.0568, 0.0577, 0.0561, 0.0641, 0.0641, 0.0593, 0.0592, 0.0831, 0.0661, 0.0605, 0.059, 0.0549, 0.0491, 0.0547, 0.0572, 0.0511, 0.0546, 0.0589, 0.0546, 0.0396, 0.0505, 0.0505, 0.067, 0.0544, 0.051, 0.051, 0.0537, 0.0572, 0.0541, 0.0549, 0.0549, 0.0349, 0.0665, 0.0605, 0.0544, 0.0544, 0.052, 0.0576, 0.0571, 0.0489, 0.0497, 0.0562, 0.0543, 0.0759, 0.0629, 0.0629, 0.0627, 0.0592, 0.0584, 0.0458, 0.04, 0.045, 0.0468, 0.0553, 0.055, 0.0408, 0.0596, 0.0568, 0.0488, 0.0506, 0.059, 0.0509, 0.0516, 0.0562, 0.0524, 0.0538, 0.0576] loss_box_reg: [0.2147, 0.2379, 0.2868, 0.3267, 0.3912, 0.3942, 0.4301, 0.4052, 0.3653, 0.3427, 0.3131, 0.34, 0.4331, 0.3582, 0.3284, 0.3169, 0.2924, 0.3116, 0.3224, 0.3381, 0.3021, 0.2882, 0.2583, 0.2464, 0.164, 0.1996, 0.2095, 0.2423, 0.2579, 0.2311, 0.1954, 0.2112, 0.2273, 0.2543, 0.229, 0.2203, 0.2405, 0.2405, 0.2179, 0.1863, 0.1596, 0.1435, 0.1637, 0.1603, 0.1522, 0.1524, 0.1553, 0.1607, 0.1808, 0.141, 0.1371, 0.1525, 0.1423, 0.151, 0.1642, 0.1642, 0.1797, 0.1665, 0.1484, 0.157, 0.2281, 0.1591, 0.1513, 0.1293, 0.1387, 0.1387, 0.1391, 0.1596, 0.1553, 0.141, 0.1553, 0.1553, 0.1187, 0.1692, 0.1513, 0.1435, 0.124, 0.1356, 0.1436, 0.1498, 0.1474, 0.1447, 0.1422, 0.1447, 0.2355, 0.1588, 0.1329, 0.1329, 0.145, 0.1449, 0.1309, 0.1323, 0.1323, 0.1187, 0.1433, 0.1433, 0.1332, 0.1332, 0.1366, 0.1442, 0.1368, 0.127, 0.1517, 0.1531, 0.1561, 0.1549, 0.1162, 0.1145, 0.1314, 0.1302, 0.1255, 0.1255, 0.1315, 0.1315, 0.1355, 0.1366, 0.1418, 0.1488, 0.1465, 0.1438, 0.1825, 0.1609, 0.1324, 0.1326, 0.1342, 0.1142, 0.1351, 0.1417, 0.1399, 0.1412, 0.1298, 0.1339, 0.1188, 0.1402, 0.1385, 0.1542, 0.1448, 0.1161, 0.117, 0.1204, 0.138, 0.1389, 0.1389, 0.1521, 0.1038, 0.157, 0.1419, 0.1148, 0.125, 0.1283, 0.1465, 0.1352, 0.1314, 0.1516, 0.1371, 0.1312, 0.1537, 0.1263, 0.1498, 0.151, 0.144, 0.1418, 0.1195, 0.1138, 0.1137, 0.1282, 0.1544, 0.1402, 0.1007, 0.1381, 0.1373, 0.1191, 0.1414, 0.1524, 0.1188, 0.1317, 0.1317, 0.1268, 0.1322, 0.1392] loss_objectness: [0.199, 0.199, 0.1409, 0.0952, 0.0787, 0.0389, 0.0233, 0.0335, 0.0278, 0.0169, 0.0202, 0.0206, 0.0605, 0.0207, 0.0156, 0.013, 0.0105, 0.0105, 0.0101, 0.0101, 0.0116, 0.0108, 0.0071, 0.0071, 0.0093, 0.009, 0.0053, 0.0041, 0.005, 0.0036, 0.0022, 0.0021, 0.0038, 0.0042, 0.0056, 0.0059, 0.0026, 0.004, 0.0032, 0.0025, 0.0024, 0.0024, 0.003, 0.003, 0.0022, 0.0014, 0.002, 0.0022, 0.0028, 0.0026, 0.0018, 0.0017, 0.0013, 0.0013, 0.0013, 0.0014, 0.0022, 0.002, 0.0011, 0.0011, 0.0012, 0.002, 0.0018, 0.0017, 0.0009, 0.0011, 0.002, 0.0018, 0.0015, 0.001, 0.0022, 0.0025, 0.0005, 0.0016, 0.0016, 0.0011, 0.0011, 0.0009, 0.0013, 0.0008, 0.0009, 0.0014, 0.0012, 0.0013, 0.0032, 0.0014, 0.001, 0.001, 0.0011, 0.0011, 0.0019, 0.0016, 0.0016, 0.0011, 0.0008, 0.0008, 0.0031, 0.0021, 0.0012, 0.0018, 0.0013, 0.0009, 0.0011, 0.0019, 0.002, 0.0014, 0.001, 0.0007, 0.003, 0.0014, 0.0013, 0.0012, 0.0013, 0.0012, 0.0009, 0.0008, 0.0012, 0.0014, 0.0011, 0.001, 0.0013, 0.0017, 0.0014, 0.0014, 0.0009, 0.0006, 0.0018, 0.0015, 0.0014, 0.0015, 0.0015, 0.0016, 0.0006, 0.0007, 0.001, 0.0018, 0.0014, 0.0013, 0.0012, 0.0008, 0.0008, 0.001, 0.0016, 0.0016, 0.0007, 0.0015, 0.0012, 0.0011, 0.0015, 0.0013, 0.0012, 0.0012, 0.0008, 0.0008, 0.0011, 0.0011, 0.0072, 0.0011, 0.0011, 0.0017, 0.0014, 0.001, 0.0009, 0.0008, 0.0016, 0.0014, 0.0011, 0.0011, 0.0017, 0.0017, 0.0013, 0.0013, 0.0014, 0.0014, 0.0006, 0.0015, 0.0015, 0.0014, 0.0014, 0.0015] loss_rpn_box_reg: [0.0125, 0.0125, 0.0126, 0.0171, 0.0166, 0.0166, 0.0144, 0.0122, 0.0118, 0.0118, 0.0158, 0.0162, 0.0243, 0.0142, 0.0127, 0.0101, 0.0084, 0.0091, 0.0093, 0.0099, 0.0117, 0.0123, 0.0104, 0.0104, 0.0059, 0.0101, 0.0081, 0.0075, 0.0089, 0.0072, 0.0061, 0.0065, 0.0073, 0.0096, 0.0096, 0.0087, 0.0097, 0.0098, 0.0085, 0.0073, 0.0056, 0.0054, 0.006, 0.0055, 0.0044, 0.0058, 0.0062, 0.0062, 0.0073, 0.003, 0.004, 0.0047, 0.005, 0.0058, 0.0073, 0.0065, 0.0065, 0.0059, 0.0048, 0.0042, 0.0139, 0.0043, 0.0047, 0.0047, 0.0037, 0.0047, 0.0048, 0.0058, 0.0058, 0.0046, 0.005, 0.005, 0.0059, 0.0063, 0.0063, 0.0041, 0.0042, 0.0046, 0.0044, 0.0053, 0.0053, 0.005, 0.005, 0.005, 0.0097, 0.006, 0.0058, 0.0049, 0.0059, 0.0053, 0.0042, 0.0052, 0.0048, 0.0042, 0.0052, 0.005, 0.003, 0.0032, 0.0042, 0.0046, 0.0042, 0.0042, 0.0052, 0.0065, 0.0068, 0.0061, 0.0048, 0.0039, 0.0056, 0.0046, 0.004, 0.004, 0.0043, 0.0052, 0.0054, 0.0043, 0.0053, 0.0055, 0.0054, 0.0053, 0.0083, 0.0055, 0.0047, 0.006, 0.0041, 0.0038, 0.0056, 0.0048, 0.0036, 0.0036, 0.0035, 0.0034, 0.0046, 0.0045, 0.0049, 0.006, 0.0052, 0.0046, 0.0043, 0.004, 0.0053, 0.0053, 0.0058, 0.0065, 0.0036, 0.0043, 0.0047, 0.0047, 0.0047, 0.0041, 0.0057, 0.0057, 0.0042, 0.0044, 0.0056, 0.0056, 0.014, 0.0049, 0.0051, 0.0073, 0.005, 0.005, 0.0039, 0.0031, 0.0032, 0.0035, 0.0049, 0.0049, 0.0047, 0.0062, 0.0049, 0.0049, 0.0059, 0.0051, 0.0037, 0.0042, 0.0052, 0.004, 0.0053, 0.0056] model_time: [] evaluator_time: [] total_time: []
import matplotlib.pyplot as plt
# Number of epochs or iterations
start_epoch = 1
end_epoch = 20
epochs = list(range(start_epoch, end_epoch + 1))
# Create a figure and axis for plotting
plt.figure(figsize=(10, 6))
# Plotting all loss metrics
plt.plot(epochs, metrics["loss"][:len(epochs)], label='Total Loss', marker='o')
plt.plot(epochs, metrics["loss_classifier"][:len(epochs)], label='Classifier Loss', marker='o')
plt.plot(epochs, metrics["loss_box_reg"][:len(epochs)], label='Box Reg. Loss', marker='o')
plt.plot(epochs, metrics["loss_objectness"][:len(epochs)], label='Objectness Loss', marker='o')
plt.plot(epochs, metrics["loss_rpn_box_reg"][:len(epochs)], label='RPN Box Reg. Loss', marker='o')
# Adding titles and labels
plt.title('Loss Metrics Over Epochs')
plt.xlabel('Epoch')
plt.ylabel('Loss Value')
plt.legend()
# Show the plot
plt.show()
import matplotlib.pyplot as plt
# Number of epochs or iterations
epochs = list(range(1, len(metrics["AR_1"]) + 1))
# Create a figure and axis for plotting
plt.figure(figsize=(10, 6))
# Plotting recall metrics
plt.plot(epochs, metrics["AR_1"], label='AR [maxDets=1]', marker='o')
plt.plot(epochs, metrics["AR_10"], label='AR [maxDets=10]', marker='o')
plt.plot(epochs, metrics["AR_100"], label='AR [maxDets=100]', marker='o')
plt.plot(epochs, metrics["AR_small"], label='AR [area=small]', marker='o')
plt.plot(epochs, metrics["AR_medium"], label='AR [area=medium]', marker='o')
plt.plot(epochs, metrics["AR_large"], label='AR [area=large]', marker='o')
# Adding titles and labels
plt.title('Average Recall (AR) Metrics Over Epochs')
plt.xlabel('Epoch')
plt.ylabel('Recall Value')
plt.legend()
# Show the plot
plt.show()
import matplotlib.pyplot as plt
# Assuming precision_values and recall_values are provided and correctly matched
precision_values = metrics["AP"] # List of precision values
recall_values = metrics["AR_100"] # List of recall values corresponding to some IoU or similar metric
# Sort the data by recall since the precision-recall curve expects this.
sorted_indices = sorted(range(len(recall_values)), key=lambda k: recall_values[k])
precision_values = [precision_values[i] for i in sorted_indices]
recall_values = [recall_values[i] for i in sorted_indices]
# To ensure the plot fully spans, check starts and ends
if recall_values[0] > 0:
recall_values.insert(0, 0)
precision_values.insert(0, precision_values[0])
if recall_values[-1] < 1:
recall_values.append(1)
precision_values.append(precision_values[-1])
# Create the step plot for the precision-recall curve
plt.figure(figsize=(10, 5))
plt.step(recall_values, precision_values, where='post', label='Precision vs. Recall (Purple)', color='red', linewidth=2.5)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision vs. Recall Curve -- Adam and SGD')
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.grid(True)
plt.legend()
# Draw a horizontal line at y=1
plt.axhline(y=1, color='blue', linestyle='-', linewidth=3.5, label='Adam and sgd ')
# Draw a vertical purple line from y=1 to the precision at the first recall step
if recall_values[0] == 0:
first_non_zero_precision = next(p for p in precision_values if p > 0)
plt.vlines(x=recall_values[1], ymin=1, ymax=first_non_zero_precision, colors='red', linestyles='-', linewidth=2.5)
# Add a legend to clarify line meanings
plt.legend(title='Legend', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
import re
# Define dictionaries to hold your data
metrics = {
"AR_1": [],
"AR_10": [],
"AR_100": [],
"AR_small": [],
"AR_medium": [],
"AR_large": [],
"AP": [], # Add AP metric
"AP_50": [], # Add AP_50 metric
"AP_75": [], # Add AP_75 metric
"loss": [], # Add loss metric
"loss_classifier": [], # Add loss_classifier metric
"loss_box_reg": [], # Add loss_box_reg metric
"loss_objectness": [], # Add loss_objectness metric
"loss_rpn_box_reg": [], # Add loss_rpn_box_reg metric
"model_time": [], # Add model_time metric
"evaluator_time": [], # Add evaluator_time metric
"total_time": [] # Add total_time metric
}
# Regex patterns to extract data
pattern_ar_1 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 1 \] = (\d+\.\d+)")
pattern_ar_10 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 10 \] = (\d+\.\d+)")
pattern_ar_100 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_small = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= small \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_medium = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area=medium \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_large = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= large \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_50 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_75 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.75 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_loss = re.compile(r"loss: (\d+\.\d+)") # Pattern for loss
pattern_loss_classifier = re.compile(r"loss_classifier: (\d+\.\d+)") # Pattern for loss_classifier
pattern_loss_box_reg = re.compile(r"loss_box_reg: (\d+\.\d+)") # Pattern for loss_box_reg
pattern_loss_objectness = re.compile(r"loss_objectness: (\d+\.\d+)") # Pattern for loss_objectness
pattern_loss_rpn_box_reg = re.compile(r"loss_rpn_box_reg: (\d+\.\d+)")# Pattern for loss_rpn_box_reg
pattern_model_time = re.compile(r"Model time:\s+(\d+\.\d+)") # Pattern for model_time
pattern_evaluator_time = re.compile(r"Evaluator time:\s+(\d+\.\d+)")# Pattern for evaluator_time
pattern_total_time = re.compile(r"Total time:\s+(\d+\.\d+)") # Pattern for total_time
# Read the log file
with open('adelta.txt', 'r') as file:
file_content = file.read()
# Handling AR matches
metrics["AR_1"].extend([float(x) for x in pattern_ar_1.findall(file_content)])
metrics["AR_10"].extend([float(x) for x in pattern_ar_10.findall(file_content)])
metrics["AR_100"].extend([float(x) for x in pattern_ar_100.findall(file_content)])
metrics["AR_small"].extend([float(x) for x in pattern_ar_small.findall(file_content)])
metrics["AR_medium"].extend([float(x) for x in pattern_ar_medium.findall(file_content)])
metrics["AR_large"].extend([float(x) for x in pattern_ar_large.findall(file_content)])
# Handling AP matches
metrics["AP"].extend([float(x) for x in pattern_ap.findall(file_content)])
metrics["AP_50"].extend([float(x) for x in pattern_ap_50.findall(file_content)])
metrics["AP_75"].extend([float(x) for x in pattern_ap_75.findall(file_content)])
# Handling loss matches
metrics["loss"].extend([float(x) for x in pattern_loss.findall(file_content)])
# Handling loss_classifier matches
metrics["loss_classifier"].extend([float(x) for x in pattern_loss_classifier.findall(file_content)])
# Handling loss_box_reg matches
metrics["loss_box_reg"].extend([float(x) for x in pattern_loss_box_reg.findall(file_content)])
# Handling loss_objectness matches
metrics["loss_objectness"].extend([float(x) for x in pattern_loss_objectness.findall(file_content)])
# Handling loss_rpn_box_reg matches
metrics["loss_rpn_box_reg"].extend([float(x) for x in pattern_loss_rpn_box_reg.findall(file_content)])
# Handling model_time matches
metrics["model_time"].extend([float(x) for x in pattern_model_time.findall(file_content)])
# Handling evaluator_time matches
metrics["evaluator_time"].extend([float(x) for x in pattern_evaluator_time.findall(file_content)])
# Handling total_time matches
metrics["total_time"].extend([float(x) for x in pattern_total_time.findall(file_content)])
# Print the collected metrics to verify
for key, value in metrics.items():
print(f"{key}: {value}")
AR_1: [0.001, 0.006, 0.012, 0.013, 0.012, 0.013, 0.013, 0.013, 0.014, 0.014, 0.014, 0.014, 0.014, 0.014, 0.014] AR_10: [0.011, 0.041, 0.077, 0.082, 0.084, 0.085, 0.085, 0.086, 0.086, 0.086, 0.086, 0.086, 0.086, 0.086, 0.086] AR_100: [0.016, 0.051, 0.104, 0.114, 0.119, 0.122, 0.122, 0.124, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125] AR_small: [0.014, 0.107, 0.135, 0.152, 0.14, 0.154, 0.155, 0.156, 0.157, 0.157, 0.15, 0.15, 0.15, 0.15, 0.15] AR_medium: [0.006, 0.038, 0.139, 0.147, 0.151, 0.154, 0.153, 0.154, 0.155, 0.155, 0.155, 0.155, 0.155, 0.155, 0.155] AR_large: [0.008, 0.0, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025] AP: [0.001, 0.011, 0.025, 0.026, 0.027, 0.028, 0.028, 0.029, 0.029, 0.029, 0.028, 0.028, 0.028, 0.028, 0.028] AP_50: [] AP_75: [] loss: [4.0759, 3.36, 3.1378, 2.9602, 2.9602, 2.8197, 2.6611, 2.4501, 2.0663, 1.8681, 1.6345, 1.6345, 1.1351, 1.1702, 1.0701, 0.9767, 0.9272, 0.8314, 0.8384, 0.9086, 0.8851, 0.8026, 0.9113, 0.9113, 0.9163, 0.9163, 0.9086, 0.9709, 0.9518, 0.8419, 0.8895, 0.9628, 0.9628, 0.8832, 0.8006, 0.8006, 1.0471, 1.0512, 1.0315, 0.9557, 0.9115, 0.9662, 0.9121, 0.8367, 1.0318, 0.9981, 0.9981, 1.0118, 1.06, 0.9999, 0.9513, 0.9513, 0.9775, 0.8397, 0.8397, 0.8745, 0.8969, 0.8969, 0.9435, 0.9443, 0.8089, 0.8925, 0.8562, 0.9047, 0.9314, 0.8906, 0.9511, 1.0254, 1.006, 1.0086, 0.9006, 0.9371, 1.2885, 1.0491, 0.9693, 0.9755, 0.9755, 0.9509, 1.0108, 0.8464, 0.8103, 0.9766, 1.032, 1.032, 1.4218, 1.0095, 1.0162, 1.0405, 0.8868, 0.9059, 0.9683, 0.9683, 0.9465, 0.8826, 0.8826, 0.8776, 1.4561, 1.0857, 1.0857, 0.9701, 0.9484, 0.8402, 0.8402, 0.9218, 0.9491, 0.9491, 0.9549, 0.9549, 0.6304, 0.9999, 0.9999, 1.0741, 1.0208, 0.963, 0.9851, 0.9349, 0.882, 0.9438, 0.9698, 0.9841, 0.8985, 0.9647, 0.8716, 0.8716, 0.9804, 0.9462, 0.968, 0.9544, 0.9351, 0.9478, 0.931, 0.9491, 0.755, 0.9419, 1.0021, 0.9841, 0.8633, 0.9035, 0.8914, 0.8592, 0.8975, 0.8975, 0.9923, 0.8953, 1.2637, 1.1292, 0.9303, 0.8486, 0.877, 0.9349, 0.826, 0.8678, 0.9021, 0.9521, 0.9724, 0.9875, 1.0455, 0.9119, 1.1687, 1.0182, 0.8346, 0.837, 0.8682, 0.9294, 0.9831, 0.934, 0.9029, 0.9214, 0.6101, 0.9464, 0.9644, 0.9311, 0.9757, 1.0226, 0.8524, 0.9881, 0.9881, 1.038, 0.9153, 0.9153] loss_classifier: [2.5687, 2.6055, 2.5849, 2.5095, 2.4297, 2.2737, 2.1325, 1.9114, 1.7363, 1.3998, 1.1006, 1.0552, 0.7006, 0.7006, 0.5925, 0.5344, 0.5014, 0.4376, 0.4454, 0.4537, 0.4499, 0.4275, 0.4669, 0.4463, 0.4803, 0.4786, 0.4786, 0.513, 0.5024, 0.4381, 0.4577, 0.5025, 0.5025, 0.4909, 0.4076, 0.4076, 0.5305, 0.5372, 0.5223, 0.4734, 0.466, 0.5089, 0.4642, 0.4401, 0.5163, 0.4935, 0.5089, 0.5108, 0.5243, 0.5243, 0.4843, 0.4843, 0.4883, 0.4366, 0.4268, 0.4384, 0.483, 0.4673, 0.4673, 0.4772, 0.4197, 0.4646, 0.4325, 0.4699, 0.4938, 0.4345, 0.4656, 0.5239, 0.5202, 0.4859, 0.4366, 0.4366, 0.6427, 0.5128, 0.4798, 0.4994, 0.4994, 0.4764, 0.5083, 0.421, 0.4081, 0.4935, 0.4976, 0.4948, 0.6696, 0.484, 0.484, 0.4935, 0.4536, 0.4536, 0.4741, 0.4857, 0.4834, 0.4435, 0.4553, 0.4504, 0.648, 0.5417, 0.5324, 0.5126, 0.473, 0.4363, 0.4242, 0.4895, 0.4907, 0.4843, 0.4989, 0.4989, 0.3522, 0.5041, 0.4866, 0.5166, 0.5166, 0.4896, 0.5003, 0.4848, 0.4339, 0.4703, 0.4863, 0.4922, 0.4508, 0.4805, 0.456, 0.4485, 0.4722, 0.4719, 0.5065, 0.495, 0.4696, 0.4696, 0.4724, 0.4825, 0.4027, 0.4774, 0.5084, 0.5064, 0.4275, 0.4459, 0.4438, 0.4411, 0.4754, 0.4754, 0.4808, 0.4682, 0.5983, 0.5538, 0.4809, 0.4486, 0.4563, 0.4563, 0.423, 0.4651, 0.4667, 0.4933, 0.4933, 0.5226, 0.5254, 0.4604, 0.5784, 0.4869, 0.4342, 0.4228, 0.4398, 0.4642, 0.4986, 0.4654, 0.4607, 0.4654, 0.2887, 0.4903, 0.4903, 0.4634, 0.4914, 0.5268, 0.4663, 0.4924, 0.5019, 0.5328, 0.4701, 0.4701] loss_box_reg: [0.2849, 0.2849, 0.1922, 0.158, 0.1765, 0.2803, 0.2608, 0.2562, 0.2166, 0.2123, 0.2507, 0.2609, 0.2811, 0.3579, 0.3021, 0.3021, 0.2982, 0.2701, 0.2669, 0.2953, 0.2936, 0.2936, 0.3101, 0.3214, 0.3495, 0.3385, 0.3239, 0.386, 0.3826, 0.3406, 0.3665, 0.3798, 0.3798, 0.3771, 0.3359, 0.3424, 0.4053, 0.442, 0.4315, 0.3947, 0.3792, 0.4089, 0.3875, 0.369, 0.398, 0.3643, 0.4189, 0.4203, 0.4688, 0.4057, 0.395, 0.374, 0.403, 0.3437, 0.3625, 0.3625, 0.3493, 0.3838, 0.3838, 0.3838, 0.3246, 0.3785, 0.3719, 0.3729, 0.3978, 0.3787, 0.3845, 0.4201, 0.4178, 0.4331, 0.3988, 0.4087, 0.5884, 0.46, 0.4007, 0.3828, 0.4114, 0.4237, 0.437, 0.3655, 0.3511, 0.3786, 0.4053, 0.4036, 0.6648, 0.4434, 0.4568, 0.3845, 0.3749, 0.3783, 0.4048, 0.4071, 0.4071, 0.3466, 0.3637, 0.3742, 0.5642, 0.4551, 0.4345, 0.4303, 0.4157, 0.3572, 0.3395, 0.3836, 0.3861, 0.3844, 0.3844, 0.3844, 0.2296, 0.428, 0.4208, 0.4718, 0.4274, 0.4103, 0.4112, 0.3944, 0.3741, 0.3989, 0.417, 0.4309, 0.3753, 0.4047, 0.3817, 0.3739, 0.4234, 0.3912, 0.41, 0.4145, 0.3985, 0.3947, 0.414, 0.418, 0.3002, 0.406, 0.4273, 0.4191, 0.3595, 0.385, 0.3755, 0.3648, 0.3745, 0.3767, 0.3905, 0.3905, 0.6241, 0.4678, 0.4035, 0.3564, 0.3876, 0.3876, 0.3398, 0.3602, 0.3757, 0.3953, 0.4017, 0.4037, 0.459, 0.381, 0.5096, 0.4253, 0.3361, 0.3397, 0.3525, 0.3764, 0.4131, 0.4099, 0.3724, 0.3794, 0.206, 0.3871, 0.4017, 0.3893, 0.4169, 0.433, 0.3546, 0.3968, 0.3968, 0.4397, 0.3913, 0.39] loss_objectness: [1.1855, 0.3081, 0.2341, 0.2463, 0.3229, 0.3229, 0.2339, 0.1492, 0.1413, 0.1271, 0.1404, 0.1482, 0.1311, 0.0991, 0.0991, 0.1022, 0.1022, 0.1048, 0.1022, 0.095, 0.0816, 0.0677, 0.0627, 0.0627, 0.0612, 0.0614, 0.0696, 0.0691, 0.0632, 0.0576, 0.0575, 0.0557, 0.0529, 0.0475, 0.0467, 0.0454, 0.0849, 0.0744, 0.0517, 0.0451, 0.0462, 0.0467, 0.0473, 0.0472, 0.0539, 0.0574, 0.0501, 0.0478, 0.0506, 0.0481, 0.0481, 0.0565, 0.0565, 0.0421, 0.0399, 0.0562, 0.0634, 0.0489, 0.0452, 0.044, 0.052, 0.0391, 0.0382, 0.0393, 0.0637, 0.0479, 0.047, 0.0485, 0.0518, 0.0493, 0.0493, 0.0494, 0.0387, 0.0419, 0.0451, 0.0528, 0.0538, 0.0538, 0.0433, 0.0396, 0.035, 0.032, 0.045, 0.0528, 0.066, 0.0545, 0.052, 0.052, 0.0384, 0.0384, 0.0562, 0.0417, 0.0392, 0.0408, 0.0492, 0.0539, 0.2178, 0.0692, 0.0358, 0.0384, 0.0396, 0.0386, 0.0426, 0.0431, 0.0524, 0.0402, 0.0385, 0.0552, 0.0376, 0.0353, 0.0473, 0.0532, 0.0451, 0.0382, 0.0423, 0.0452, 0.0316, 0.0394, 0.0481, 0.0504, 0.0546, 0.0524, 0.0483, 0.0557, 0.0491, 0.0443, 0.0416, 0.0416, 0.0418, 0.0492, 0.0444, 0.0472, 0.043, 0.0463, 0.0445, 0.0389, 0.0427, 0.0444, 0.0444, 0.0479, 0.0491, 0.0581, 0.056, 0.0488, 0.031, 0.0361, 0.0362, 0.0408, 0.0418, 0.0422, 0.0408, 0.041, 0.0441, 0.0518, 0.0518, 0.0518, 0.049, 0.0483, 0.0587, 0.057, 0.0438, 0.0368, 0.0405, 0.0451, 0.0478, 0.05, 0.0532, 0.052, 0.0904, 0.0569, 0.0541, 0.0541, 0.0434, 0.0329, 0.0433, 0.0472, 0.0436, 0.0436, 0.0432, 0.0465] loss_rpn_box_reg: [0.0368, 0.0216, 0.0178, 0.0178, 0.0201, 0.0216, 0.0205, 0.0181, 0.0146, 0.0156, 0.0193, 0.0211, 0.0222, 0.0222, 0.0203, 0.0179, 0.0179, 0.0159, 0.0159, 0.0155, 0.013, 0.0123, 0.0149, 0.0173, 0.0254, 0.014, 0.0162, 0.0168, 0.0146, 0.0143, 0.0192, 0.0189, 0.0128, 0.013, 0.013, 0.0102, 0.0264, 0.0263, 0.0218, 0.0136, 0.0135, 0.0165, 0.0128, 0.0111, 0.0163, 0.016, 0.0158, 0.0167, 0.0161, 0.0161, 0.016, 0.0195, 0.0212, 0.0129, 0.0093, 0.0174, 0.0193, 0.0137, 0.0137, 0.0137, 0.0127, 0.0127, 0.0129, 0.0143, 0.0147, 0.0133, 0.015, 0.0179, 0.0204, 0.0192, 0.014, 0.017, 0.0187, 0.0158, 0.0154, 0.0178, 0.0154, 0.016, 0.016, 0.0116, 0.0116, 0.0135, 0.0163, 0.0193, 0.0214, 0.015, 0.015, 0.0181, 0.0131, 0.015, 0.0206, 0.0165, 0.0139, 0.0126, 0.0123, 0.0123, 0.0261, 0.0177, 0.0145, 0.0125, 0.0114, 0.0118, 0.0169, 0.0175, 0.0136, 0.0136, 0.016, 0.0171, 0.011, 0.0127, 0.0161, 0.0161, 0.0175, 0.0177, 0.0188, 0.0173, 0.0098, 0.0126, 0.0124, 0.0142, 0.0178, 0.0178, 0.0144, 0.0143, 0.0168, 0.0157, 0.0134, 0.0155, 0.0155, 0.0123, 0.0123, 0.0167, 0.009, 0.0138, 0.0159, 0.0142, 0.0124, 0.0146, 0.0118, 0.0142, 0.0185, 0.018, 0.0173, 0.0153, 0.0103, 0.0126, 0.0159, 0.0159, 0.015, 0.0155, 0.012, 0.0118, 0.0143, 0.0153, 0.0148, 0.0186, 0.0121, 0.0173, 0.0221, 0.0168, 0.011, 0.0137, 0.0125, 0.0128, 0.0153, 0.0152, 0.0149, 0.0129, 0.0251, 0.0206, 0.0206, 0.0179, 0.0167, 0.0153, 0.0134, 0.0161, 0.0161, 0.0171, 0.0161, 0.0154] model_time: [] evaluator_time: [] total_time: []
# Example: Extracting the average recall for different thresholds
recall_values_Adelta = metrics["AR_100"] # Let's use AR_100 as an example for IoU thresholds plotting
# Example: Precision vs. Recall (assuming AP data correlates with precision directly at different recalls)
precision_values_Adelta = metrics["AP"] # Direct extraction for simplicity in this example
import re
# Define dictionaries to hold your data
metrics = {
"AR_1": [],
"AR_10": [],
"AR_100": [],
"AR_small": [],
"AR_medium": [],
"AR_large": [],
"AP": [], # Add AP metric
"AP_50": [], # Add AP_50 metric
"AP_75": [], # Add AP_75 metric
"loss": [], # Add loss metric
"loss_classifier": [], # Add loss_classifier metric
"loss_box_reg": [], # Add loss_box_reg metric
"loss_objectness": [], # Add loss_objectness metric
"loss_rpn_box_reg": [], # Add loss_rpn_box_reg metric
"model_time": [], # Add model_time metric
"evaluator_time": [], # Add evaluator_time metric
"total_time": [] # Add total_time metric
}
# Regex patterns to extract data
pattern_ar_1 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 1 \] = (\d+\.\d+)")
pattern_ar_10 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 10 \] = (\d+\.\d+)")
pattern_ar_100 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_small = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= small \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_medium = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area=medium \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_large = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= large \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_50 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_75 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.75 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_loss = re.compile(r"loss: (\d+\.\d+)") # Pattern for loss
pattern_loss_classifier = re.compile(r"loss_classifier: (\d+\.\d+)") # Pattern for loss_classifier
pattern_loss_box_reg = re.compile(r"loss_box_reg: (\d+\.\d+)") # Pattern for loss_box_reg
pattern_loss_objectness = re.compile(r"loss_objectness: (\d+\.\d+)") # Pattern for loss_objectness
pattern_loss_rpn_box_reg = re.compile(r"loss_rpn_box_reg: (\d+\.\d+)")# Pattern for loss_rpn_box_reg
pattern_model_time = re.compile(r"Model time:\s+(\d+\.\d+)") # Pattern for model_time
pattern_evaluator_time = re.compile(r"Evaluator time:\s+(\d+\.\d+)")# Pattern for evaluator_time
pattern_total_time = re.compile(r"Total time:\s+(\d+\.\d+)") # Pattern for total_time
# Read the log file
with open('eva adam.txt', 'r') as file:
file_content = file.read()
# Handling AR matches
metrics["AR_1"].extend([float(x) for x in pattern_ar_1.findall(file_content)])
metrics["AR_10"].extend([float(x) for x in pattern_ar_10.findall(file_content)])
metrics["AR_100"].extend([float(x) for x in pattern_ar_100.findall(file_content)])
metrics["AR_small"].extend([float(x) for x in pattern_ar_small.findall(file_content)])
metrics["AR_medium"].extend([float(x) for x in pattern_ar_medium.findall(file_content)])
metrics["AR_large"].extend([float(x) for x in pattern_ar_large.findall(file_content)])
# Handling AP matches
metrics["AP"].extend([float(x) for x in pattern_ap.findall(file_content)])
metrics["AP_50"].extend([float(x) for x in pattern_ap_50.findall(file_content)])
metrics["AP_75"].extend([float(x) for x in pattern_ap_75.findall(file_content)])
# Handling loss matches
metrics["loss"].extend([float(x) for x in pattern_loss.findall(file_content)])
# Handling loss_classifier matches
metrics["loss_classifier"].extend([float(x) for x in pattern_loss_classifier.findall(file_content)])
# Handling loss_box_reg matches
metrics["loss_box_reg"].extend([float(x) for x in pattern_loss_box_reg.findall(file_content)])
# Handling loss_objectness matches
metrics["loss_objectness"].extend([float(x) for x in pattern_loss_objectness.findall(file_content)])
# Handling loss_rpn_box_reg matches
metrics["loss_rpn_box_reg"].extend([float(x) for x in pattern_loss_rpn_box_reg.findall(file_content)])
# Handling model_time matches
metrics["model_time"].extend([float(x) for x in pattern_model_time.findall(file_content)])
# Handling evaluator_time matches
metrics["evaluator_time"].extend([float(x) for x in pattern_evaluator_time.findall(file_content)])
# Handling total_time matches
metrics["total_time"].extend([float(x) for x in pattern_total_time.findall(file_content)])
# Print the collected metrics to verify
for key, value in metrics.items():
print(f"{key}: {value}")
AR_1: [0.06, 0.108, 0.111, 0.176, 0.193, 0.192, 0.208, 0.21, 0.21, 0.21, 0.213, 0.215, 0.216, 0.216, 0.216] AR_10: [0.138, 0.303, 0.311, 0.483, 0.492, 0.501, 0.518, 0.518, 0.522, 0.519, 0.523, 0.524, 0.524, 0.524, 0.524] AR_100: [0.18, 0.351, 0.356, 0.545, 0.552, 0.558, 0.576, 0.576, 0.576, 0.574, 0.578, 0.579, 0.58, 0.58, 0.58] AR_small: [0.206, 0.316, 0.289, 0.438, 0.422, 0.448, 0.444, 0.444, 0.445, 0.447, 0.447, 0.448, 0.448, 0.448, 0.448] AR_medium: [0.199, 0.396, 0.416, 0.588, 0.599, 0.606, 0.623, 0.63, 0.629, 0.622, 0.626, 0.628, 0.629, 0.629, 0.629] AR_large: [0.088, 0.183, 0.246, 0.441, 0.487, 0.47, 0.492, 0.513, 0.528, 0.518, 0.532, 0.523, 0.523, 0.523, 0.523] AP: [0.086, 0.209, 0.212, 0.408, 0.429, 0.452, 0.478, 0.482, 0.483, 0.485, 0.488, 0.49, 0.491, 0.491, 0.491] AP_50: [] AP_75: [] loss: [3.4412, 1.9666, 1.0744, 0.8417, 0.8124, 0.8505, 0.9567, 0.8243, 0.8379, 0.8269, 0.7398, 0.7319, 0.8187, 0.626, 0.6369, 0.6915, 0.7575, 0.7855, 0.7067, 0.6577, 0.602, 0.5815, 0.6848, 0.5815, 0.237, 0.5648, 0.5648, 0.5875, 0.6245, 0.6245, 0.6124, 0.6184, 0.6771, 0.6771, 0.6658, 0.6658, 0.6804, 0.6202, 0.5724, 0.544, 0.4058, 0.4355, 0.4576, 0.4142, 0.3602, 0.3589, 0.3456, 0.3425, 0.5567, 0.3472, 0.3464, 0.3354, 0.3787, 0.3625, 0.358, 0.4037, 0.3903, 0.3678, 0.3634, 0.3103, 0.4193, 0.3269, 0.3544, 0.3695, 0.3503, 0.3338, 0.3008, 0.3433, 0.3123, 0.2546, 0.2826, 0.2938, 0.4073, 0.2835, 0.2868, 0.2868, 0.2755, 0.2693, 0.2315, 0.2315, 0.2799, 0.2819, 0.286, 0.286, 0.4399, 0.2756, 0.2791, 0.2799, 0.24, 0.2466, 0.2689, 0.2628, 0.2451, 0.2572, 0.2582, 0.2582, 0.3998, 0.3032, 0.2519, 0.2395, 0.242, 0.2497, 0.264, 0.2567, 0.2421, 0.2462, 0.2585, 0.2463, 0.1893, 0.2888, 0.2615, 0.2615, 0.2488, 0.2303, 0.2391, 0.2392, 0.2606, 0.2535, 0.25, 0.2444, 0.3089, 0.2657, 0.2605, 0.2682, 0.2551, 0.2229, 0.2212, 0.2255, 0.2585, 0.275, 0.2316, 0.2247, 0.2695, 0.2695, 0.2545, 0.2302, 0.2295, 0.2392, 0.2893, 0.2819, 0.2583, 0.2398, 0.2494, 0.2512, 0.3497, 0.2639, 0.2498, 0.249, 0.249, 0.25, 0.3066, 0.2656, 0.2258, 0.2576, 0.2593, 0.2605, 0.2393, 0.291, 0.2833, 0.2602, 0.2506, 0.2642, 0.2453, 0.2453, 0.2683, 0.2463, 0.2353, 0.2353, 0.341, 0.2914, 0.2498, 0.2601, 0.2729, 0.2713, 0.2197, 0.2562, 0.2723, 0.2576, 0.2431, 0.2379] loss_classifier: [2.6871, 1.1984, 0.5573, 0.4224, 0.4127, 0.43, 0.43, 0.3951, 0.3951, 0.3935, 0.3464, 0.3661, 0.3262, 0.2835, 0.2835, 0.2926, 0.2951, 0.3421, 0.3367, 0.2653, 0.2453, 0.2143, 0.2656, 0.2348, 0.0976, 0.1963, 0.2199, 0.2595, 0.3, 0.2793, 0.2727, 0.2489, 0.2657, 0.2679, 0.2378, 0.2378, 0.2065, 0.2539, 0.2272, 0.2248, 0.1643, 0.1803, 0.1871, 0.1623, 0.1426, 0.1411, 0.1264, 0.1245, 0.2793, 0.1236, 0.1226, 0.1258, 0.1354, 0.1303, 0.1295, 0.1394, 0.1387, 0.1358, 0.1216, 0.1062, 0.16, 0.139, 0.1277, 0.1277, 0.1212, 0.1079, 0.1079, 0.1082, 0.1012, 0.0837, 0.095, 0.1015, 0.1331, 0.1123, 0.1066, 0.0995, 0.0995, 0.095, 0.086, 0.0908, 0.0908, 0.1034, 0.1034, 0.1024, 0.1619, 0.1017, 0.1017, 0.0968, 0.084, 0.0838, 0.0875, 0.085, 0.0906, 0.0913, 0.095, 0.1008, 0.1506, 0.1042, 0.0926, 0.0878, 0.0923, 0.0996, 0.0942, 0.0847, 0.0903, 0.0924, 0.0851, 0.0792, 0.068, 0.1062, 0.0999, 0.0915, 0.0905, 0.0789, 0.0853, 0.0898, 0.0934, 0.0889, 0.0813, 0.0784, 0.1102, 0.0903, 0.0932, 0.0955, 0.0816, 0.0771, 0.0734, 0.0734, 0.0922, 0.0931, 0.0895, 0.0815, 0.0898, 0.0898, 0.09, 0.0824, 0.0814, 0.0881, 0.0993, 0.0993, 0.0874, 0.0854, 0.0855, 0.0885, 0.1162, 0.0837, 0.0865, 0.0865, 0.0856, 0.0856, 0.0943, 0.0877, 0.0735, 0.0903, 0.0903, 0.0918, 0.0915, 0.1009, 0.1029, 0.0801, 0.0801, 0.0905, 0.0866, 0.0866, 0.0866, 0.0811, 0.0811, 0.085, 0.1312, 0.1028, 0.0872, 0.088, 0.0955, 0.0907, 0.0854, 0.0945, 0.0945, 0.0889, 0.0874, 0.0836] loss_box_reg: [0.242, 0.3116, 0.3418, 0.351, 0.3445, 0.3445, 0.3505, 0.2874, 0.3641, 0.3835, 0.3031, 0.2775, 0.3955, 0.2487, 0.2845, 0.3091, 0.3427, 0.3934, 0.3516, 0.2943, 0.27, 0.278, 0.3175, 0.2664, 0.1113, 0.2968, 0.3092, 0.3092, 0.3041, 0.3083, 0.2801, 0.2801, 0.3387, 0.3083, 0.331, 0.3307, 0.3854, 0.2812, 0.2722, 0.2523, 0.2174, 0.2227, 0.2408, 0.2172, 0.208, 0.2026, 0.2009, 0.1985, 0.2358, 0.2126, 0.2008, 0.1787, 0.2111, 0.2021, 0.2104, 0.2228, 0.2209, 0.2143, 0.2001, 0.1927, 0.2258, 0.1936, 0.2139, 0.2237, 0.2038, 0.1861, 0.1812, 0.1927, 0.1895, 0.1607, 0.1764, 0.1781, 0.2401, 0.1738, 0.1738, 0.16, 0.1557, 0.1473, 0.1282, 0.1212, 0.181, 0.1826, 0.168, 0.168, 0.2502, 0.1496, 0.1614, 0.1614, 0.1456, 0.1572, 0.1658, 0.1587, 0.1557, 0.154, 0.1645, 0.1679, 0.2245, 0.1803, 0.159, 0.1522, 0.1517, 0.1467, 0.1487, 0.1487, 0.1393, 0.1409, 0.1527, 0.1509, 0.1151, 0.1607, 0.1388, 0.1451, 0.1451, 0.1414, 0.1478, 0.143, 0.1607, 0.1581, 0.1495, 0.1495, 0.1835, 0.1476, 0.1476, 0.1592, 0.1583, 0.1324, 0.1324, 0.1316, 0.1622, 0.1622, 0.1439, 0.1439, 0.1705, 0.1633, 0.1567, 0.1386, 0.1414, 0.148, 0.1679, 0.1679, 0.1529, 0.1406, 0.1472, 0.1508, 0.2069, 0.1679, 0.1379, 0.1386, 0.1379, 0.131, 0.1809, 0.1641, 0.1359, 0.1535, 0.1555, 0.1555, 0.1408, 0.1593, 0.1593, 0.1569, 0.1573, 0.161, 0.1528, 0.1528, 0.1559, 0.1417, 0.1483, 0.1483, 0.1974, 0.1671, 0.1375, 0.1574, 0.1624, 0.1412, 0.1366, 0.1455, 0.165, 0.156, 0.1418, 0.1418] loss_objectness: [0.4842, 0.1556, 0.1066, 0.0588, 0.0425, 0.0415, 0.0581, 0.0613, 0.0537, 0.0555, 0.0724, 0.0815, 0.0776, 0.0706, 0.0516, 0.0429, 0.0464, 0.0324, 0.0316, 0.0441, 0.0439, 0.0288, 0.0325, 0.0311, 0.0123, 0.0258, 0.0264, 0.0277, 0.0231, 0.0225, 0.0352, 0.0333, 0.0374, 0.0435, 0.0319, 0.0302, 0.0661, 0.0324, 0.0196, 0.0185, 0.0146, 0.0157, 0.0167, 0.0118, 0.0113, 0.0113, 0.0081, 0.0081, 0.0143, 0.0074, 0.0074, 0.0095, 0.0086, 0.0069, 0.0088, 0.0093, 0.0059, 0.0059, 0.0059, 0.0061, 0.0096, 0.0089, 0.0081, 0.0066, 0.0055, 0.006, 0.0064, 0.0056, 0.0042, 0.0051, 0.0046, 0.0046, 0.0098, 0.0055, 0.0049, 0.0046, 0.0041, 0.0033, 0.0033, 0.0036, 0.0036, 0.004, 0.0045, 0.0034, 0.0036, 0.0052, 0.0047, 0.003, 0.003, 0.0029, 0.0025, 0.003, 0.0023, 0.0025, 0.0034, 0.003, 0.0096, 0.0038, 0.0033, 0.0033, 0.0044, 0.0039, 0.0035, 0.0038, 0.0039, 0.0037, 0.0033, 0.0033, 0.0013, 0.0035, 0.004, 0.0042, 0.0039, 0.0034, 0.0037, 0.0033, 0.0022, 0.003, 0.0036, 0.0037, 0.0038, 0.0035, 0.0034, 0.0035, 0.0033, 0.0032, 0.0029, 0.0038, 0.0034, 0.0034, 0.0026, 0.0025, 0.0016, 0.002, 0.0022, 0.0025, 0.0024, 0.0032, 0.0031, 0.0029, 0.0028, 0.0026, 0.0029, 0.0036, 0.006, 0.0025, 0.0034, 0.0047, 0.0039, 0.0031, 0.004, 0.0038, 0.0026, 0.0026, 0.003, 0.003, 0.001, 0.0021, 0.0035, 0.0033, 0.0028, 0.0033, 0.0037, 0.0042, 0.0035, 0.0035, 0.002, 0.0021, 0.0029, 0.0027, 0.0027, 0.003, 0.0032, 0.0025, 0.0021, 0.0028, 0.0033, 0.0022, 0.0022, 0.0022] loss_rpn_box_reg: [0.0278, 0.0162, 0.0162, 0.0132, 0.0132, 0.0172, 0.0223, 0.0214, 0.0173, 0.0216, 0.0199, 0.0199, 0.0195, 0.0216, 0.0199, 0.0173, 0.0196, 0.0207, 0.0179, 0.0164, 0.0216, 0.0215, 0.0176, 0.0149, 0.0158, 0.0164, 0.0157, 0.0142, 0.0142, 0.0139, 0.0139, 0.018, 0.0227, 0.0217, 0.0217, 0.02, 0.0224, 0.017, 0.0166, 0.0143, 0.0138, 0.0144, 0.0136, 0.0135, 0.0128, 0.0097, 0.0088, 0.0088, 0.0273, 0.0107, 0.009, 0.0081, 0.0104, 0.0091, 0.0093, 0.0093, 0.0095, 0.0107, 0.0103, 0.0099, 0.024, 0.008, 0.0104, 0.0114, 0.0103, 0.0099, 0.0101, 0.0085, 0.0065, 0.007, 0.0077, 0.0076, 0.0243, 0.008, 0.0081, 0.0082, 0.0082, 0.0079, 0.0067, 0.0056, 0.0075, 0.0078, 0.0069, 0.0085, 0.0242, 0.0107, 0.0082, 0.0076, 0.0067, 0.0061, 0.0078, 0.0078, 0.0069, 0.0075, 0.0078, 0.0075, 0.0151, 0.013, 0.008, 0.007, 0.007, 0.007, 0.0069, 0.0069, 0.0065, 0.0062, 0.0065, 0.0062, 0.005, 0.0061, 0.006, 0.0081, 0.0103, 0.0077, 0.0073, 0.0072, 0.0072, 0.0065, 0.006, 0.0062, 0.0114, 0.0075, 0.0076, 0.0078, 0.0069, 0.0054, 0.0052, 0.006, 0.008, 0.008, 0.0062, 0.0065, 0.0075, 0.0095, 0.008, 0.0062, 0.0055, 0.0062, 0.008, 0.0071, 0.0071, 0.0066, 0.007, 0.0072, 0.0205, 0.0052, 0.0062, 0.0075, 0.0075, 0.0074, 0.0091, 0.0085, 0.0066, 0.0069, 0.0068, 0.0065, 0.0061, 0.0082, 0.0085, 0.0079, 0.0074, 0.0074, 0.0067, 0.0079, 0.0085, 0.0064, 0.0069, 0.007, 0.0096, 0.0082, 0.0078, 0.0063, 0.0082, 0.0082, 0.0065, 0.0077, 0.0102, 0.0074, 0.0055, 0.0055] model_time: [] evaluator_time: [] total_time: []
# Example: Extracting the average recall for different thresholds
recall_values_Adam = metrics["AR_100"] # Let's use AR_100 as an example for IoU thresholds plotting
# Example: Precision vs. Recall (assuming AP data correlates with precision directly at different recalls)
precision_values_Adam = metrics["AP"] # Direct extraction for simplicity in this example
import re
# Define dictionaries to hold your data
metrics = {
"AR_1": [],
"AR_10": [],
"AR_100": [],
"AR_small": [],
"AR_medium": [],
"AR_large": [],
"AP": [], # Add AP metric
"AP_50": [], # Add AP_50 metric
"AP_75": [], # Add AP_75 metric
"loss": [], # Add loss metric
"loss_classifier": [], # Add loss_classifier metric
"loss_box_reg": [], # Add loss_box_reg metric
"loss_objectness": [], # Add loss_objectness metric
"loss_rpn_box_reg": [], # Add loss_rpn_box_reg metric
"model_time": [], # Add model_time metric
"evaluator_time": [], # Add evaluator_time metric
"total_time": [] # Add total_time metric
}
# Regex patterns to extract data
pattern_ar_1 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 1 \] = (\d+\.\d+)")
pattern_ar_10 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 10 \] = (\d+\.\d+)")
pattern_ar_100 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_small = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= small \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_medium = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area=medium \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_large = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= large \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_50 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_75 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.75 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_loss = re.compile(r"loss: (\d+\.\d+)") # Pattern for loss
pattern_loss_classifier = re.compile(r"loss_classifier: (\d+\.\d+)") # Pattern for loss_classifier
pattern_loss_box_reg = re.compile(r"loss_box_reg: (\d+\.\d+)") # Pattern for loss_box_reg
pattern_loss_objectness = re.compile(r"loss_objectness: (\d+\.\d+)") # Pattern for loss_objectness
pattern_loss_rpn_box_reg = re.compile(r"loss_rpn_box_reg: (\d+\.\d+)")# Pattern for loss_rpn_box_reg
pattern_model_time = re.compile(r"Model time:\s+(\d+\.\d+)") # Pattern for model_time
pattern_evaluator_time = re.compile(r"Evaluator time:\s+(\d+\.\d+)")# Pattern for evaluator_time
pattern_total_time = re.compile(r"Total time:\s+(\d+\.\d+)") # Pattern for total_time
# Read the log file
with open('eva adamn_sgd.txt', 'r') as file:
file_content = file.read()
# Handling AR matches
metrics["AR_1"].extend([float(x) for x in pattern_ar_1.findall(file_content)])
metrics["AR_10"].extend([float(x) for x in pattern_ar_10.findall(file_content)])
metrics["AR_100"].extend([float(x) for x in pattern_ar_100.findall(file_content)])
metrics["AR_small"].extend([float(x) for x in pattern_ar_small.findall(file_content)])
metrics["AR_medium"].extend([float(x) for x in pattern_ar_medium.findall(file_content)])
metrics["AR_large"].extend([float(x) for x in pattern_ar_large.findall(file_content)])
# Handling AP matches
metrics["AP"].extend([float(x) for x in pattern_ap.findall(file_content)])
metrics["AP_50"].extend([float(x) for x in pattern_ap_50.findall(file_content)])
metrics["AP_75"].extend([float(x) for x in pattern_ap_75.findall(file_content)])
# Handling loss matches
metrics["loss"].extend([float(x) for x in pattern_loss.findall(file_content)])
# Handling loss_classifier matches
metrics["loss_classifier"].extend([float(x) for x in pattern_loss_classifier.findall(file_content)])
# Handling loss_box_reg matches
metrics["loss_box_reg"].extend([float(x) for x in pattern_loss_box_reg.findall(file_content)])
# Handling loss_objectness matches
metrics["loss_objectness"].extend([float(x) for x in pattern_loss_objectness.findall(file_content)])
# Handling loss_rpn_box_reg matches
metrics["loss_rpn_box_reg"].extend([float(x) for x in pattern_loss_rpn_box_reg.findall(file_content)])
# Handling model_time matches
metrics["model_time"].extend([float(x) for x in pattern_model_time.findall(file_content)])
# Handling evaluator_time matches
metrics["evaluator_time"].extend([float(x) for x in pattern_evaluator_time.findall(file_content)])
# Handling total_time matches
metrics["total_time"].extend([float(x) for x in pattern_total_time.findall(file_content)])
# Print the collected metrics to verify
for key, value in metrics.items():
print(f"{key}: {value}")
AR_1: [0.073, 0.007, 0.045, 0.071, 0.105, 0.123, 0.153, 0.157, 0.154, 0.159, 0.153, 0.161, 0.159, 0.166, 0.165] AR_10: [0.178, 0.056, 0.174, 0.259, 0.315, 0.375, 0.433, 0.442, 0.438, 0.449, 0.447, 0.451, 0.454, 0.455, 0.464] AR_100: [0.223, 0.101, 0.226, 0.323, 0.371, 0.441, 0.501, 0.506, 0.494, 0.509, 0.509, 0.513, 0.513, 0.517, 0.529] AR_small: [0.246, 0.115, 0.235, 0.334, 0.35, 0.376, 0.413, 0.434, 0.441, 0.447, 0.455, 0.445, 0.441, 0.45, 0.463] AR_medium: [0.178, 0.154, 0.278, 0.374, 0.373, 0.504, 0.553, 0.532, 0.533, 0.544, 0.544, 0.55, 0.547, 0.552, 0.569] AR_large: [0.221, 0.0, 0.09, 0.06, 0.159, 0.237, 0.329, 0.317, 0.307, 0.322, 0.289, 0.339, 0.368, 0.362, 0.375] AP: [0.098, 0.032, 0.095, 0.173, 0.225, 0.29, 0.347, 0.355, 0.353, 0.361, 0.366, 0.371, 0.372, 0.38, 0.391] AP_50: [] AP_75: [] loss: [3.3583, 2.3279, 1.209, 0.9238, 0.879, 0.941, 0.8082, 0.6632, 0.5934, 0.8766, 0.7816, 0.6924, 0.5595, 0.9238, 0.9238, 0.8937, 0.8828, 0.8035, 0.7846, 0.7665, 0.7328, 0.8004, 0.8303, 0.8303, 0.5037, 0.7679, 0.7679, 0.6997, 0.6524, 0.6844, 0.6844, 0.7528, 0.8539, 0.8539, 0.6873, 0.6611, 0.9499, 0.8302, 0.6848, 0.7425, 0.7538, 0.6383, 0.6383, 0.5951, 0.6758, 0.6085, 0.5904, 0.6085, 0.9558, 0.7101, 0.6309, 0.6323, 0.6639, 0.575, 0.633, 0.6562, 0.6114, 0.5727, 0.5472, 0.5416, 0.468, 0.4746, 0.5318, 0.6038, 0.581, 0.5266, 0.5092, 0.4859, 0.5286, 0.5525, 0.5126, 0.4816, 0.2004, 0.379, 0.379, 0.3963, 0.4557, 0.4646, 0.4693, 0.4249, 0.4432, 0.4432, 0.3908, 0.3908, 0.5793, 0.3872, 0.3806, 0.4133, 0.3974, 0.35, 0.36, 0.3652, 0.3829, 0.4413, 0.4413, 0.4344, 0.2473, 0.3371, 0.3752, 0.3752, 0.371, 0.371, 0.3706, 0.3706, 0.3642, 0.3939, 0.3779, 0.3779, 0.2582, 0.4245, 0.4, 0.3995, 0.405, 0.3894, 0.3969, 0.4003, 0.3622, 0.3177, 0.341, 0.3467, 0.3358, 0.3452, 0.3529, 0.3529, 0.3148, 0.3396, 0.3396, 0.3545, 0.3728, 0.3848, 0.4097, 0.3852, 0.3552, 0.3552, 0.3827, 0.3661, 0.296, 0.2937, 0.3493, 0.3441, 0.3341, 0.3917, 0.3986, 0.3917, 0.3296, 0.3754, 0.3603, 0.344, 0.334, 0.3332, 0.3676, 0.3456, 0.3438, 0.3364, 0.3178, 0.3118, 0.3092, 0.3466, 0.3585, 0.3632, 0.3632, 0.3369, 0.3831, 0.3831, 0.3283, 0.3066, 0.3514, 0.3514, 0.4111, 0.3929, 0.3124, 0.3136, 0.3399, 0.3399, 0.3414, 0.3355, 0.3644, 0.3639, 0.3578, 0.3658] loss_classifier: [2.5736, 1.5769, 0.664, 0.4708, 0.4517, 0.4517, 0.3806, 0.3152, 0.3131, 0.3554, 0.3472, 0.3055, 0.2305, 0.3809, 0.4395, 0.4452, 0.4144, 0.3222, 0.3764, 0.3365, 0.3365, 0.3582, 0.3423, 0.3094, 0.2505, 0.263, 0.3391, 0.3233, 0.3024, 0.3101, 0.3198, 0.3344, 0.3837, 0.3838, 0.2968, 0.2968, 0.3417, 0.3417, 0.3111, 0.3458, 0.282, 0.2502, 0.2553, 0.2605, 0.2742, 0.2477, 0.2427, 0.2726, 0.493, 0.2657, 0.2392, 0.2409, 0.275, 0.2283, 0.2227, 0.2589, 0.2353, 0.2379, 0.2376, 0.2112, 0.1478, 0.2071, 0.2175, 0.235, 0.235, 0.2094, 0.1943, 0.1813, 0.1831, 0.1898, 0.1921, 0.1829, 0.085, 0.1573, 0.1573, 0.1639, 0.1723, 0.1774, 0.1746, 0.1655, 0.1613, 0.1654, 0.1517, 0.1499, 0.2718, 0.1535, 0.142, 0.1405, 0.1418, 0.1315, 0.1315, 0.1331, 0.1563, 0.1581, 0.159, 0.152, 0.0932, 0.1366, 0.1365, 0.1365, 0.1305, 0.1323, 0.1415, 0.1418, 0.1418, 0.1465, 0.1397, 0.1397, 0.0994, 0.1346, 0.1377, 0.1344, 0.1295, 0.1551, 0.1575, 0.1554, 0.1343, 0.1343, 0.1387, 0.1387, 0.129, 0.1273, 0.1237, 0.1161, 0.1141, 0.1231, 0.1231, 0.1419, 0.1448, 0.1362, 0.1414, 0.1362, 0.1059, 0.1188, 0.1198, 0.1198, 0.1169, 0.1132, 0.1264, 0.1179, 0.1228, 0.1369, 0.1348, 0.1388, 0.1243, 0.1278, 0.122, 0.122, 0.1333, 0.1333, 0.129, 0.1198, 0.1352, 0.1323, 0.1175, 0.1122, 0.0881, 0.1134, 0.1336, 0.1322, 0.1311, 0.1256, 0.1317, 0.1336, 0.121, 0.1091, 0.1135, 0.1213, 0.1705, 0.1241, 0.1204, 0.1137, 0.113, 0.1125, 0.1187, 0.1128, 0.1185, 0.1278, 0.1257, 0.1278] loss_box_reg: [0.4333, 0.359, 0.359, 0.3461, 0.3577, 0.3279, 0.3105, 0.2753, 0.2408, 0.3787, 0.3829, 0.3446, 0.2976, 0.42, 0.3396, 0.257, 0.2087, 0.1765, 0.2107, 0.178, 0.207, 0.2739, 0.2363, 0.2845, 0.1813, 0.3321, 0.3321, 0.2844, 0.238, 0.2446, 0.2572, 0.3064, 0.3581, 0.3581, 0.2919, 0.2749, 0.4621, 0.3616, 0.3178, 0.3276, 0.3276, 0.3137, 0.2984, 0.2664, 0.3144, 0.3144, 0.2899, 0.31, 0.3486, 0.3435, 0.293, 0.297, 0.297, 0.2813, 0.2909, 0.3541, 0.2933, 0.2812, 0.2812, 0.2812, 0.2861, 0.2416, 0.2739, 0.289, 0.2642, 0.2642, 0.2546, 0.2525, 0.2902, 0.3005, 0.2651, 0.2651, 0.0903, 0.1895, 0.2113, 0.2222, 0.2258, 0.2445, 0.2554, 0.2321, 0.2495, 0.2633, 0.2001, 0.2096, 0.2628, 0.2187, 0.2103, 0.2212, 0.1994, 0.1885, 0.208, 0.2129, 0.2107, 0.2467, 0.2542, 0.2392, 0.1216, 0.1766, 0.1974, 0.2118, 0.2202, 0.2105, 0.1955, 0.1955, 0.2009, 0.2182, 0.2145, 0.2145, 0.1455, 0.2167, 0.2237, 0.2237, 0.2357, 0.2282, 0.2105, 0.2174, 0.2125, 0.1839, 0.1886, 0.1878, 0.1879, 0.1987, 0.2196, 0.2196, 0.1836, 0.2026, 0.1967, 0.2021, 0.2101, 0.2158, 0.2242, 0.219, 0.2213, 0.209, 0.209, 0.202, 0.1678, 0.1621, 0.1996, 0.1896, 0.181, 0.2154, 0.2154, 0.2154, 0.1716, 0.2398, 0.221, 0.1879, 0.1853, 0.1968, 0.2198, 0.1968, 0.1954, 0.1908, 0.1908, 0.1895, 0.1915, 0.1997, 0.1997, 0.2048, 0.2053, 0.1936, 0.2342, 0.2201, 0.1896, 0.1808, 0.2078, 0.2078, 0.2171, 0.2171, 0.1759, 0.1806, 0.189, 0.2136, 0.1988, 0.1946, 0.1995, 0.2112, 0.2131, 0.2145] loss_objectness: [0.3133, 0.1724, 0.0846, 0.0666, 0.0476, 0.0535, 0.0697, 0.0606, 0.0489, 0.0441, 0.043, 0.0425, 0.0189, 0.0522, 0.0792, 0.16, 0.178, 0.1685, 0.1683, 0.1311, 0.1122, 0.1272, 0.1451, 0.1452, 0.0628, 0.1093, 0.1086, 0.1138, 0.0968, 0.0753, 0.0772, 0.0596, 0.0577, 0.0537, 0.0533, 0.0537, 0.1011, 0.0448, 0.0435, 0.0473, 0.041, 0.041, 0.044, 0.0458, 0.0472, 0.0452, 0.0452, 0.041, 0.0946, 0.0472, 0.0401, 0.0392, 0.037, 0.0317, 0.0316, 0.0412, 0.0382, 0.037, 0.0378, 0.0353, 0.0217, 0.025, 0.0252, 0.0325, 0.0323, 0.031, 0.0306, 0.0248, 0.0236, 0.0242, 0.0218, 0.0218, 0.0186, 0.0203, 0.022, 0.023, 0.0183, 0.0139, 0.0151, 0.0179, 0.015, 0.0155, 0.0155, 0.0147, 0.0261, 0.0147, 0.0147, 0.0172, 0.018, 0.0163, 0.0157, 0.0148, 0.0132, 0.0141, 0.0126, 0.0126, 0.0231, 0.0176, 0.0141, 0.0131, 0.0123, 0.0123, 0.011, 0.011, 0.0159, 0.0168, 0.0126, 0.0129, 0.0069, 0.012, 0.0132, 0.0121, 0.012, 0.0118, 0.0138, 0.0166, 0.0145, 0.0109, 0.0117, 0.014, 0.0118, 0.0131, 0.0114, 0.0101, 0.0125, 0.0125, 0.0125, 0.016, 0.0178, 0.012, 0.0107, 0.0107, 0.0111, 0.0128, 0.0125, 0.0122, 0.0103, 0.0102, 0.0117, 0.0141, 0.0119, 0.0131, 0.0115, 0.0113, 0.0247, 0.0135, 0.012, 0.0107, 0.0133, 0.0113, 0.0129, 0.0132, 0.012, 0.01, 0.0087, 0.0087, 0.0168, 0.0141, 0.0132, 0.0106, 0.0106, 0.0104, 0.0078, 0.009, 0.0094, 0.0094, 0.011, 0.011, 0.0078, 0.0107, 0.0101, 0.0101, 0.0109, 0.0117, 0.0109, 0.0098, 0.0101, 0.0102, 0.0088, 0.0079] loss_rpn_box_reg: [0.0381, 0.02, 0.0191, 0.0139, 0.014, 0.0218, 0.0244, 0.0175, 0.0122, 0.0196, 0.02, 0.0163, 0.0124, 0.0273, 0.0273, 0.0256, 0.0408, 0.0305, 0.0292, 0.0338, 0.021, 0.0222, 0.0222, 0.0225, 0.0091, 0.0268, 0.028, 0.0254, 0.0232, 0.0267, 0.024, 0.0235, 0.0265, 0.0274, 0.0234, 0.018, 0.045, 0.0273, 0.0234, 0.0217, 0.0217, 0.0182, 0.0172, 0.0172, 0.0216, 0.0232, 0.0188, 0.0193, 0.0195, 0.0195, 0.021, 0.021, 0.0238, 0.0209, 0.0194, 0.0197, 0.0213, 0.0217, 0.0183, 0.0182, 0.0124, 0.0146, 0.0154, 0.0162, 0.014, 0.0175, 0.0192, 0.0164, 0.0197, 0.0217, 0.017, 0.017, 0.0066, 0.0126, 0.0126, 0.0141, 0.015, 0.0146, 0.0173, 0.0166, 0.013, 0.013, 0.0096, 0.0106, 0.0186, 0.0158, 0.0118, 0.0121, 0.0115, 0.011, 0.0108, 0.0103, 0.0103, 0.0127, 0.0147, 0.0147, 0.0095, 0.0103, 0.0125, 0.0138, 0.0119, 0.0106, 0.0138, 0.0138, 0.0133, 0.0131, 0.0105, 0.0105, 0.0064, 0.0103, 0.0096, 0.0085, 0.0138, 0.0138, 0.0119, 0.0149, 0.0109, 0.0097, 0.01, 0.01, 0.0071, 0.0083, 0.0111, 0.0108, 0.0104, 0.0113, 0.0107, 0.0101, 0.0101, 0.0096, 0.0098, 0.0092, 0.0169, 0.0129, 0.0116, 0.0103, 0.0082, 0.0082, 0.0116, 0.0105, 0.0105, 0.0124, 0.0124, 0.0134, 0.009, 0.0115, 0.0115, 0.0107, 0.0101, 0.0101, 0.0118, 0.0099, 0.0099, 0.0101, 0.0082, 0.0085, 0.0128, 0.0102, 0.0102, 0.0103, 0.0099, 0.0092, 0.0096, 0.0122, 0.0101, 0.0087, 0.009, 0.0119, 0.0156, 0.0111, 0.01, 0.009, 0.009, 0.0099, 0.0099, 0.0096, 0.0096, 0.0093, 0.0121, 0.0115] model_time: [] evaluator_time: [] total_time: []
# Example: Extracting the average recall for different thresholds
recall_values_ADAMNSGD = metrics["AR_100"] # Let's use AR_100 as an example for IoU thresholds plotting
# Example: Precision vs. Recall (assuming AP data correlates with precision directly at different recalls)
precision_values_ADAMNSGD = metrics["AP"] # Direct extraction for simplicity in this example
import re
# Define dictionaries to hold your data
metrics = {
"AR_1": [],
"AR_10": [],
"AR_100": [],
"AR_small": [],
"AR_medium": [],
"AR_large": [],
"AP": [], # Add AP metric
"AP_50": [], # Add AP_50 metric
"AP_75": [], # Add AP_75 metric
"loss": [], # Add loss metric
"loss_classifier": [], # Add loss_classifier metric
"loss_box_reg": [], # Add loss_box_reg metric
"loss_objectness": [], # Add loss_objectness metric
"loss_rpn_box_reg": [], # Add loss_rpn_box_reg metric
"model_time": [], # Add model_time metric
"evaluator_time": [], # Add evaluator_time metric
"total_time": [] # Add total_time metric
}
# Regex patterns to extract data
pattern_ar_1 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 1 \] = (\d+\.\d+)")
pattern_ar_10 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 10 \] = (\d+\.\d+)")
pattern_ar_100 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_small = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= small \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_medium = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area=medium \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_large = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= large \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_50 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_75 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.75 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_loss = re.compile(r"loss: (\d+\.\d+)") # Pattern for loss
pattern_loss_classifier = re.compile(r"loss_classifier: (\d+\.\d+)") # Pattern for loss_classifier
pattern_loss_box_reg = re.compile(r"loss_box_reg: (\d+\.\d+)") # Pattern for loss_box_reg
pattern_loss_objectness = re.compile(r"loss_objectness: (\d+\.\d+)") # Pattern for loss_objectness
pattern_loss_rpn_box_reg = re.compile(r"loss_rpn_box_reg: (\d+\.\d+)")# Pattern for loss_rpn_box_reg
pattern_model_time = re.compile(r"Model time:\s+(\d+\.\d+)") # Pattern for model_time
pattern_evaluator_time = re.compile(r"Evaluator time:\s+(\d+\.\d+)")# Pattern for evaluator_time
pattern_total_time = re.compile(r"Total time:\s+(\d+\.\d+)") # Pattern for total_time
# Read the log file
with open('eva rsmprob.txt', 'r') as file:
file_content = file.read()
# Handling AR matches
metrics["AR_1"].extend([float(x) for x in pattern_ar_1.findall(file_content)])
metrics["AR_10"].extend([float(x) for x in pattern_ar_10.findall(file_content)])
metrics["AR_100"].extend([float(x) for x in pattern_ar_100.findall(file_content)])
metrics["AR_small"].extend([float(x) for x in pattern_ar_small.findall(file_content)])
metrics["AR_medium"].extend([float(x) for x in pattern_ar_medium.findall(file_content)])
metrics["AR_large"].extend([float(x) for x in pattern_ar_large.findall(file_content)])
# Handling AP matches
metrics["AP"].extend([float(x) for x in pattern_ap.findall(file_content)])
metrics["AP_50"].extend([float(x) for x in pattern_ap_50.findall(file_content)])
metrics["AP_75"].extend([float(x) for x in pattern_ap_75.findall(file_content)])
# Handling loss matches
metrics["loss"].extend([float(x) for x in pattern_loss.findall(file_content)])
# Handling loss_classifier matches
metrics["loss_classifier"].extend([float(x) for x in pattern_loss_classifier.findall(file_content)])
# Handling loss_box_reg matches
metrics["loss_box_reg"].extend([float(x) for x in pattern_loss_box_reg.findall(file_content)])
# Handling loss_objectness matches
metrics["loss_objectness"].extend([float(x) for x in pattern_loss_objectness.findall(file_content)])
# Handling loss_rpn_box_reg matches
metrics["loss_rpn_box_reg"].extend([float(x) for x in pattern_loss_rpn_box_reg.findall(file_content)])
# Handling model_time matches
metrics["model_time"].extend([float(x) for x in pattern_model_time.findall(file_content)])
# Handling evaluator_time matches
metrics["evaluator_time"].extend([float(x) for x in pattern_evaluator_time.findall(file_content)])
# Handling total_time matches
metrics["total_time"].extend([float(x) for x in pattern_total_time.findall(file_content)])
# Print the collected metrics to verify
for key, value in metrics.items():
print(f"{key}: {value}")
AR_1: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AR_10: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AR_100: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AR_small: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AR_medium: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AR_large: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AP: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AP_50: [] AP_75: [] loss: [3.2039, 1.2988, 1.2517, 1.0549, 1.3226, 2.6148, 2.235, 0.9199, 0.6987, 0.5503, 0.6267, 0.6827, 1.452, 258.9862, 5199.8223, 2652.1675, 1029.4604, 660.6842, 426.3805, 1013.4094, 840.2881, 199.0619, 2028.7905, 2464.8901, 3665.436, 1106.7623, 472.3109, 121.1059, 133.583, 273.1735, 365.2442, 594.5722, 490.288, 517.9962, 480.0167, 315.5242, 304.2431, 272.1476, 265.4117, 169.659, 148.1319, 142.2928, 133.3797, 141.7962, 97.769, 89.6644, 91.671, 91.671, 66.1785, 110.0946, 110.0946, 109.8829, 83.0998, 79.7844, 88.3089, 76.0205, 60.9173, 47.0268, 46.4205, 46.4205, 20.0014, 41.9883, 41.9883, 36.4438, 35.3091, 45.7666, 47.3924, 48.2683, 47.3047, 40.0958, 40.0958, 37.3095, 24.8814, 37.0075, 44.093, 57.8508, 36.9282, 32.768, 47.7588, 51.282, 35.5225, 50.4645, 34.583, 34.583, 87.8664, 47.3212, 39.5393, 38.5752, 42.6587, 50.4157, 34.5905, 29.1215, 35.2129, 37.3594, 38.2908, 48.2291, 15.9957, 32.101, 32.2809, 33.1148, 38.0942, 36.8604, 44.3082, 56.3628, 39.0102, 34.5572, 49.5937, 53.068, 55.8726, 36.021, 36.021, 47.9818, 56.1572, 40.9915, 40.7521, 41.9558, 38.1649, 39.4567, 46.5877, 45.1522, 136.4492, 33.3319, 33.3319, 37.1256, 30.8479, 50.0852, 40.0831, 40.0831, 43.0268, 37.6172, 34.2634, 38.6925, 52.8316, 52.8316, 43.8346, 37.588, 37.4579, 40.121, 42.872, 47.5396, 36.6762, 36.6762, 38.1807, 41.9239, 22.133, 41.8287, 32.3787, 30.8075, 36.132, 38.142, 47.5172, 39.287, 39.6601, 44.6805, 42.819, 42.819, 44.5011, 44.2257, 44.2257, 39.663, 39.663, 37.3944, 37.3944, 48.4363, 42.7073, 35.5755, 38.7166, 38.7166, 7.2644, 40.6111, 41.9498, 44.8585, 54.1296, 45.0768, 36.1728, 41.8349, 35.9192, 33.5233, 41.9988, 41.9988] loss_classifier: [2.6346, 0.7136, 0.5494, 0.631, 0.7431, 1.1095, 0.6001, 0.3719, 0.2646, 0.2011, 0.2911, 0.3058, 0.6604, 127.6931, 2014.8184, 1474.1609, 611.085, 430.4982, 143.6905, 778.9881, 403.803, 57.0748, 410.3585, 1564.9734, 3275.9192, 562.6231, 244.4862, 41.7808, 69.0693, 69.0693, 124.5114, 283.9956, 126.4839, 175.8158, 99.8471, 89.9003, 84.9447, 39.4093, 43.5565, 35.6502, 14.4577, 12.6049, 10.6868, 8.9238, 9.0629, 10.799, 10.799, 10.799, 2.641, 9.2976, 9.2976, 11.828, 12.7831, 12.8533, 14.3843, 5.5285, 0.5171, 0.2698, 0.2538, 0.2067, 0.1053, 0.2323, 0.292, 0.2598, 0.3117, 0.8353, 0.8437, 0.7768, 0.6391, 0.5869, 0.7374, 0.7374, 0.8751, 0.8751, 0.8575, 0.951, 1.207, 1.207, 1.3659, 1.3659, 1.2666, 1.0201, 1.1253, 1.0971, 0.7757, 1.4586, 1.2743, 0.9598, 0.9598, 1.0065, 0.7998, 0.7629, 0.9716, 0.9789, 1.1156, 1.1156, 0.3973, 1.5712, 0.6473, 0.647, 0.73, 0.9232, 0.9232, 1.0805, 1.2735, 0.9454, 0.6847, 0.7807, 0.9155, 0.7631, 0.5396, 0.5169, 0.6345, 1.1223, 0.7274, 0.5666, 0.9652, 0.6309, 0.5047, 0.5047, 0.7131, 1.4028, 0.758, 0.6061, 0.5191, 0.4484, 0.4169, 0.6742, 0.7473, 0.6455, 0.7205, 1.053, 0.1815, 0.524, 0.5493, 0.9131, 1.0893, 0.604, 0.5619, 0.5431, 0.7054, 0.7671, 0.7514, 0.7198, 0.2661, 0.4315, 0.4703, 0.6102, 0.5346, 0.3968, 0.4551, 0.6406, 0.721, 0.721, 0.4876, 0.7151, 2.7536, 0.5256, 0.7182, 0.7264, 0.5614, 0.7112, 0.8154, 0.8154, 0.7326, 0.7326, 0.6136, 0.5983, 0.4406, 0.4624, 0.5975, 0.9981, 0.9576, 0.4658, 0.5545, 0.5278, 0.5948, 0.6733, 0.7239, 0.6733] loss_box_reg: [0.2353, 0.2786, 0.269, 0.218, 0.2201, 0.2412, 0.1415, 0.1484, 0.1104, 0.1104, 0.1404, 0.1531, 0.4759, 100.0327, 2918.8621, 1091.9497, 374.0753, 89.7603, 44.3117, 142.968, 67.4619, 25.554, 74.8636, 123.4845, 63.6991, 90.5225, 85.9072, 22.7789, 33.9993, 79.6419, 80.6085, 191.6195, 82.9989, 82.9989, 123.933, 120.3518, 82.5549, 107.1224, 88.7669, 55.9581, 39.1762, 47.2105, 38.1073, 38.1073, 35.096, 29.3037, 29.0876, 25.1067, 28.5581, 35.5208, 35.5208, 24.4844, 21.4768, 15.9272, 14.1213, 4.2127, 0.5805, 0.3479, 0.3883, 0.3031, 0.0933, 0.5077, 0.6207, 0.5993, 0.8865, 0.7469, 0.3043, 0.5655, 0.7198, 0.6141, 0.6148, 0.6622, 1.3191, 1.0455, 0.8651, 0.951, 0.8505, 1.2339, 1.7621, 1.954, 1.891, 0.896, 1.0179, 1.0179, 0.3214, 1.4097, 1.644, 1.0913, 0.6641, 0.9827, 0.9682, 1.5312, 1.5312, 1.5428, 1.6116, 1.1863, 0.4962, 1.5819, 1.5546, 1.1354, 1.1214, 1.1698, 1.137, 1.137, 1.3677, 1.1661, 1.5164, 2.0435, 0.7995, 0.7818, 0.4097, 0.7757, 1.454, 1.543, 2.0573, 1.7006, 1.7006, 0.5337, 0.5314, 0.5314, 1.0747, 2.5008, 1.2173, 0.763, 1.5854, 1.1914, 0.9465, 1.9267, 1.3503, 0.886, 1.0591, 2.4034, 0.4922, 0.9764, 1.1809, 1.4377, 1.4377, 1.2593, 1.2123, 0.9506, 1.4827, 1.1549, 0.8284, 0.8284, 0.2869, 0.7968, 0.9784, 1.3047, 0.4441, 0.6667, 0.6767, 0.7314, 2.0792, 1.5198, 0.6095, 0.6377, 10.0774, 1.6258, 1.5482, 0.8544, 0.7318, 1.7637, 1.9316, 1.5628, 1.3432, 1.3305, 0.5408, 0.5408, 1.2378, 1.9378, 1.6083, 1.9661, 1.7528, 0.6312, 1.5292, 0.67, 0.8445, 0.9395, 0.9588, 0.8385] loss_objectness: [0.3166, 0.3166, 0.2239, 0.1345, 0.2049, 0.6097, 0.6692, 0.3157, 0.1908, 0.1656, 0.2006, 0.2081, 0.2343, 24.7305, 210.1121, 139.8561, 93.0817, 88.0877, 109.976, 96.8082, 109.258, 109.258, 100.7363, 123.8959, 234.6715, 47.2851, 43.4546, 30.6498, 25.5532, 35.9543, 61.5172, 125.6146, 125.6146, 131.6742, 77.258, 61.8435, 105.421, 75.0196, 75.0196, 66.9722, 67.2271, 55.7569, 46.074, 41.5079, 40.6501, 32.6587, 41.326, 35.2736, 26.6346, 35.9742, 36.0392, 40.7744, 29.0046, 31.7846, 37.6492, 37.6492, 37.019, 36.3957, 34.8001, 34.8001, 17.1295, 31.6889, 31.6889, 26.6947, 24.718, 30.9653, 32.3307, 32.3307, 31.1596, 28.8788, 26.4502, 26.3762, 18.5028, 23.6202, 27.5672, 35.4797, 25.2796, 21.0982, 30.3292, 30.8757, 23.9973, 31.3237, 25.9233, 25.9233, 60.4727, 27.7213, 27.7213, 28.337, 29.4402, 33.9286, 24.795, 19.9744, 23.3236, 25.0347, 23.8722, 25.4885, 11.7689, 18.252, 22.6618, 24.9701, 25.0311, 24.899, 30.955, 33.9098, 26.531, 22.4779, 33.7459, 35.3156, 31.7795, 24.9991, 24.9991, 33.5256, 39.335, 26.8529, 25.3522, 29.7488, 29.121, 29.2802, 24.787, 22.505, 74.9324, 25.7583, 24.6153, 24.8843, 24.2222, 34.0017, 31.6625, 26.0857, 29.3088, 25.9196, 25.9196, 26.831, 30.7382, 33.1904, 29.4245, 24.898, 24.4681, 31.2911, 33.0219, 28.7603, 23.4399, 23.1004, 23.5308, 27.0699, 16.8623, 28.223, 22.4661, 22.5891, 24.0349, 23.1181, 23.1181, 28.402, 28.3379, 28.3379, 29.2609, 29.2609, 26.2172, 27.6522, 29.7883, 26.7828, 26.6561, 24.097, 24.1391, 28.8844, 28.8844, 24.4649, 25.9364, 25.9364, 4.463, 30.6523, 31.3423, 29.2475, 33.1939, 29.7384, 26.3899, 26.3899, 23.4596, 20.6789, 27.4681, 27.847] loss_rpn_box_reg: [0.0173, 0.0292, 0.0292, 0.0252, 0.0295, 0.0607, 0.1377, 0.0653, 0.0342, 0.0246, 0.0273, 0.0323, 0.0815, 8.8204, 56.0295, 30.526, 24.7837, 20.9473, 24.6669, 24.6669, 32.2513, 20.1335, 39.7727, 52.1484, 91.1466, 10.7598, 14.1286, 12.7459, 10.1752, 12.6669, 25.2533, 37.0607, 35.4058, 31.9363, 23.2427, 14.4586, 31.3225, 12.8808, 13.7246, 25.9141, 28.7024, 22.121, 17.6979, 19.9482, 13.6175, 12.3882, 13.0908, 13.0908, 8.3447, 16.9071, 13.6858, 10.3591, 10.4131, 12.3448, 14.8587, 16.4864, 17.7755, 11.3258, 10.7662, 11.2495, 2.6733, 8.5974, 9.0488, 9.415, 8.4455, 12.1707, 13.1207, 13.373, 13.4515, 12.1153, 9.1422, 9.1422, 4.1844, 12.6925, 13.2039, 15.4171, 10.4051, 5.5364, 9.6681, 14.9676, 8.0572, 11.1851, 8.6166, 7.3149, 26.2966, 14.9454, 8.08, 8.08, 11.0174, 15.4339, 7.9839, 5.5967, 6.5975, 10.9191, 8.4591, 11.4533, 3.3333, 7.2458, 6.8732, 5.726, 8.7146, 8.7146, 9.3121, 16.4104, 7.9361, 8.1952, 12.6412, 15.0755, 22.3781, 5.7209, 5.7209, 10.3965, 10.8813, 7.0965, 6.522, 6.8678, 7.8529, 9.7066, 12.9687, 10.2175, 59.7291, 6.7655, 7.6742, 11.1457, 8.98, 9.6337, 11.9654, 11.173, 10.3651, 8.6694, 8.3527, 8.3527, 21.4197, 15.1997, 9.4829, 8.3418, 8.4908, 8.4908, 13.5257, 14.271, 6.7993, 6.8313, 13.1102, 13.1102, 4.7176, 7.5126, 7.2703, 7.9838, 8.2391, 9.0442, 16.0064, 9.2286, 9.5574, 10.2014, 9.7693, 9.6603, 5.4529, 8.7917, 12.724, 14.2219, 10.6641, 9.6952, 9.6952, 10.0771, 11.1174, 6.0084, 6.3024, 8.0078, 1.123, 9.4268, 9.5961, 12.0252, 14.3342, 12.8331, 8.3081, 8.2428, 8.9517, 9.4732, 9.7129, 11.5158] model_time: [] evaluator_time: [] total_time: []
# Example: Extracting the average recall for different thresholds
recall_values_RMSprop = metrics["AR_100"] # Let's use AR_100 as an example for IoU thresholds plotting
# Example: Precision vs. Recall (assuming AP data correlates with precision directly at different recalls)
precision_values_RMSprop = metrics["AP"] # Direct extraction for simplicity in this example
import re
# Define dictionaries to hold your data
metrics = {
"AR_1": [],
"AR_10": [],
"AR_100": [],
"AR_small": [],
"AR_medium": [],
"AR_large": [],
"AP": [], # Add AP metric
"AP_50": [], # Add AP_50 metric
"AP_75": [], # Add AP_75 metric
"loss": [], # Add loss metric
"loss_classifier": [], # Add loss_classifier metric
"loss_box_reg": [], # Add loss_box_reg metric
"loss_objectness": [], # Add loss_objectness metric
"loss_rpn_box_reg": [], # Add loss_rpn_box_reg metric
"model_time": [], # Add model_time metric
"evaluator_time": [], # Add evaluator_time metric
"total_time": [] # Add total_time metric
}
# Regex patterns to extract data
pattern_ar_1 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 1 \] = (\d+\.\d+)")
pattern_ar_10 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 10 \] = (\d+\.\d+)")
pattern_ar_100 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_small = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= small \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_medium = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area=medium \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_large = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= large \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_50 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_75 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.75 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_loss = re.compile(r"loss: (\d+\.\d+)") # Pattern for loss
pattern_loss_classifier = re.compile(r"loss_classifier: (\d+\.\d+)") # Pattern for loss_classifier
pattern_loss_box_reg = re.compile(r"loss_box_reg: (\d+\.\d+)") # Pattern for loss_box_reg
pattern_loss_objectness = re.compile(r"loss_objectness: (\d+\.\d+)") # Pattern for loss_objectness
pattern_loss_rpn_box_reg = re.compile(r"loss_rpn_box_reg: (\d+\.\d+)")# Pattern for loss_rpn_box_reg
pattern_model_time = re.compile(r"Model time:\s+(\d+\.\d+)") # Pattern for model_time
pattern_evaluator_time = re.compile(r"Evaluator time:\s+(\d+\.\d+)")# Pattern for evaluator_time
pattern_total_time = re.compile(r"Total time:\s+(\d+\.\d+)") # Pattern for total_time
# Read the log file
with open('eva sgd.txt', 'r') as file:
file_content = file.read()
# Handling AR matches
metrics["AR_1"].extend([float(x) for x in pattern_ar_1.findall(file_content)])
metrics["AR_10"].extend([float(x) for x in pattern_ar_10.findall(file_content)])
metrics["AR_100"].extend([float(x) for x in pattern_ar_100.findall(file_content)])
metrics["AR_small"].extend([float(x) for x in pattern_ar_small.findall(file_content)])
metrics["AR_medium"].extend([float(x) for x in pattern_ar_medium.findall(file_content)])
metrics["AR_large"].extend([float(x) for x in pattern_ar_large.findall(file_content)])
# Handling AP matches
metrics["AP"].extend([float(x) for x in pattern_ap.findall(file_content)])
metrics["AP_50"].extend([float(x) for x in pattern_ap_50.findall(file_content)])
metrics["AP_75"].extend([float(x) for x in pattern_ap_75.findall(file_content)])
# Handling loss matches
metrics["loss"].extend([float(x) for x in pattern_loss.findall(file_content)])
# Handling loss_classifier matches
metrics["loss_classifier"].extend([float(x) for x in pattern_loss_classifier.findall(file_content)])
# Handling loss_box_reg matches
metrics["loss_box_reg"].extend([float(x) for x in pattern_loss_box_reg.findall(file_content)])
# Handling loss_objectness matches
metrics["loss_objectness"].extend([float(x) for x in pattern_loss_objectness.findall(file_content)])
# Handling loss_rpn_box_reg matches
metrics["loss_rpn_box_reg"].extend([float(x) for x in pattern_loss_rpn_box_reg.findall(file_content)])
# Handling model_time matches
metrics["model_time"].extend([float(x) for x in pattern_model_time.findall(file_content)])
# Handling evaluator_time matches
metrics["evaluator_time"].extend([float(x) for x in pattern_evaluator_time.findall(file_content)])
# Handling total_time matches
metrics["total_time"].extend([float(x) for x in pattern_total_time.findall(file_content)])
# Print the collected metrics to verify
for key, value in metrics.items():
print(f"{key}: {value}")
AR_1: [0.146, 0.173, 0.196, 0.229, 0.228, 0.234, 0.236, 0.234, 0.235, 0.235, 0.236, 0.236, 0.236, 0.236, 0.236] AR_10: [0.359, 0.444, 0.471, 0.537, 0.54, 0.544, 0.544, 0.543, 0.543, 0.543, 0.544, 0.544, 0.544, 0.544, 0.544] AR_100: [0.426, 0.511, 0.528, 0.614, 0.615, 0.61, 0.613, 0.613, 0.614, 0.615, 0.615, 0.615, 0.615, 0.615, 0.615] AR_small: [0.478, 0.442, 0.49, 0.556, 0.561, 0.541, 0.546, 0.549, 0.547, 0.547, 0.548, 0.548, 0.548, 0.548, 0.548] AR_medium: [0.405, 0.524, 0.513, 0.596, 0.602, 0.597, 0.599, 0.599, 0.601, 0.601, 0.602, 0.602, 0.602, 0.602, 0.602] AR_large: [0.515, 0.518, 0.582, 0.663, 0.673, 0.678, 0.685, 0.673, 0.673, 0.673, 0.673, 0.673, 0.673, 0.673, 0.673] AP: [0.298, 0.404, 0.427, 0.534, 0.54, 0.543, 0.548, 0.547, 0.548, 0.548, 0.548, 0.549, 0.549, 0.549, 0.549] AP_50: [] AP_75: [] loss: [2.501, 2.1539, 1.2824, 1.1755, 0.9232, 0.903, 0.903, 0.8739, 0.7396, 0.7196, 0.6679, 0.6873, 0.9491, 0.6119, 0.5908, 0.5204, 0.4952, 0.4952, 0.4953, 0.5289, 0.512, 0.5059, 0.4005, 0.3574, 0.2529, 0.3099, 0.3128, 0.3433, 0.3896, 0.3336, 0.2878, 0.3079, 0.3821, 0.402, 0.3498, 0.3488, 0.3512, 0.3512, 0.3133, 0.2674, 0.2504, 0.2161, 0.2449, 0.2423, 0.2168, 0.2075, 0.2281, 0.2375, 0.2486, 0.2155, 0.1932, 0.2339, 0.213, 0.221, 0.2511, 0.236, 0.2556, 0.2548, 0.2291, 0.2299, 0.3329, 0.236, 0.2198, 0.2059, 0.2017, 0.2017, 0.2068, 0.2366, 0.2242, 0.2099, 0.231, 0.239, 0.1748, 0.245, 0.2194, 0.2048, 0.1756, 0.1981, 0.2118, 0.2118, 0.2146, 0.2061, 0.2095, 0.2186, 0.3377, 0.2396, 0.1979, 0.1922, 0.216, 0.212, 0.1899, 0.1954, 0.2007, 0.1722, 0.2126, 0.2127, 0.1904, 0.1936, 0.1984, 0.2162, 0.2084, 0.2009, 0.215, 0.215, 0.2228, 0.2226, 0.1706, 0.1645, 0.1976, 0.1904, 0.1882, 0.1947, 0.1974, 0.1927, 0.2107, 0.2091, 0.2091, 0.2247, 0.2146, 0.211, 0.2751, 0.228, 0.1922, 0.2016, 0.1926, 0.1722, 0.199, 0.2036, 0.2014, 0.2014, 0.1887, 0.1882, 0.1635, 0.199, 0.199, 0.2375, 0.2013, 0.167, 0.167, 0.1836, 0.2026, 0.2026, 0.1901, 0.2141, 0.143, 0.2299, 0.2113, 0.177, 0.1848, 0.1868, 0.2124, 0.2013, 0.1894, 0.2155, 0.1985, 0.1981, 0.2508, 0.1942, 0.2218, 0.2237, 0.2167, 0.2058, 0.18, 0.1557, 0.165, 0.1724, 0.2136, 0.2011, 0.1479, 0.2075, 0.1953, 0.1721, 0.2, 0.2065, 0.1814, 0.1878, 0.19, 0.1885, 0.1979, 0.2031] loss_classifier: [2.0748, 1.6329, 0.7298, 0.6021, 0.4607, 0.4386, 0.4437, 0.4131, 0.3503, 0.3234, 0.2767, 0.2767, 0.4312, 0.203, 0.203, 0.1771, 0.1601, 0.1601, 0.1667, 0.1667, 0.1708, 0.1356, 0.1111, 0.1061, 0.0738, 0.0912, 0.0937, 0.0937, 0.094, 0.076, 0.0715, 0.089, 0.1129, 0.1104, 0.0948, 0.0948, 0.0984, 0.0903, 0.0721, 0.0729, 0.0707, 0.0646, 0.0796, 0.0679, 0.0586, 0.0661, 0.0661, 0.0669, 0.0577, 0.0577, 0.0612, 0.0727, 0.0633, 0.0633, 0.0637, 0.0629, 0.0711, 0.0719, 0.0654, 0.0654, 0.0899, 0.0729, 0.0586, 0.0529, 0.0542, 0.0527, 0.0559, 0.065, 0.0663, 0.0635, 0.0614, 0.0644, 0.0497, 0.0689, 0.0646, 0.0552, 0.0488, 0.0522, 0.0613, 0.0564, 0.0564, 0.0595, 0.0598, 0.0608, 0.0893, 0.0663, 0.0567, 0.0567, 0.0579, 0.0539, 0.0537, 0.0578, 0.057, 0.0577, 0.0583, 0.0603, 0.0511, 0.0489, 0.0493, 0.058, 0.0619, 0.0622, 0.0575, 0.0575, 0.061, 0.0574, 0.0499, 0.0468, 0.0576, 0.0542, 0.0529, 0.0508, 0.0534, 0.0568, 0.0577, 0.0561, 0.0641, 0.0641, 0.0593, 0.0592, 0.0831, 0.0661, 0.0605, 0.059, 0.0549, 0.0491, 0.0547, 0.0572, 0.0511, 0.0546, 0.0589, 0.0546, 0.0396, 0.0505, 0.0505, 0.067, 0.0544, 0.051, 0.051, 0.0537, 0.0572, 0.0541, 0.0549, 0.0549, 0.0349, 0.0665, 0.0605, 0.0544, 0.0544, 0.052, 0.0576, 0.0571, 0.0489, 0.0497, 0.0562, 0.0543, 0.0759, 0.0629, 0.0629, 0.0627, 0.0592, 0.0584, 0.0458, 0.04, 0.045, 0.0468, 0.0553, 0.055, 0.0408, 0.0596, 0.0568, 0.0488, 0.0506, 0.059, 0.0509, 0.0516, 0.0562, 0.0524, 0.0538, 0.0576] loss_box_reg: [0.2147, 0.2379, 0.2868, 0.3267, 0.3912, 0.3942, 0.4301, 0.4052, 0.3653, 0.3427, 0.3131, 0.34, 0.4331, 0.3582, 0.3284, 0.3169, 0.2924, 0.3116, 0.3224, 0.3381, 0.3021, 0.2882, 0.2583, 0.2464, 0.164, 0.1996, 0.2095, 0.2423, 0.2579, 0.2311, 0.1954, 0.2112, 0.2273, 0.2543, 0.229, 0.2203, 0.2405, 0.2405, 0.2179, 0.1863, 0.1596, 0.1435, 0.1637, 0.1603, 0.1522, 0.1524, 0.1553, 0.1607, 0.1808, 0.141, 0.1371, 0.1525, 0.1423, 0.151, 0.1642, 0.1642, 0.1797, 0.1665, 0.1484, 0.157, 0.2281, 0.1591, 0.1513, 0.1293, 0.1387, 0.1387, 0.1391, 0.1596, 0.1553, 0.141, 0.1553, 0.1553, 0.1187, 0.1692, 0.1513, 0.1435, 0.124, 0.1356, 0.1436, 0.1498, 0.1474, 0.1447, 0.1422, 0.1447, 0.2355, 0.1588, 0.1329, 0.1329, 0.145, 0.1449, 0.1309, 0.1323, 0.1323, 0.1187, 0.1433, 0.1433, 0.1332, 0.1332, 0.1366, 0.1442, 0.1368, 0.127, 0.1517, 0.1531, 0.1561, 0.1549, 0.1162, 0.1145, 0.1314, 0.1302, 0.1255, 0.1255, 0.1315, 0.1315, 0.1355, 0.1366, 0.1418, 0.1488, 0.1465, 0.1438, 0.1825, 0.1609, 0.1324, 0.1326, 0.1342, 0.1142, 0.1351, 0.1417, 0.1399, 0.1412, 0.1298, 0.1339, 0.1188, 0.1402, 0.1385, 0.1542, 0.1448, 0.1161, 0.117, 0.1204, 0.138, 0.1389, 0.1389, 0.1521, 0.1038, 0.157, 0.1419, 0.1148, 0.125, 0.1283, 0.1465, 0.1352, 0.1314, 0.1516, 0.1371, 0.1312, 0.1537, 0.1263, 0.1498, 0.151, 0.144, 0.1418, 0.1195, 0.1138, 0.1137, 0.1282, 0.1544, 0.1402, 0.1007, 0.1381, 0.1373, 0.1191, 0.1414, 0.1524, 0.1188, 0.1317, 0.1317, 0.1268, 0.1322, 0.1392] loss_objectness: [0.199, 0.199, 0.1409, 0.0952, 0.0787, 0.0389, 0.0233, 0.0335, 0.0278, 0.0169, 0.0202, 0.0206, 0.0605, 0.0207, 0.0156, 0.013, 0.0105, 0.0105, 0.0101, 0.0101, 0.0116, 0.0108, 0.0071, 0.0071, 0.0093, 0.009, 0.0053, 0.0041, 0.005, 0.0036, 0.0022, 0.0021, 0.0038, 0.0042, 0.0056, 0.0059, 0.0026, 0.004, 0.0032, 0.0025, 0.0024, 0.0024, 0.003, 0.003, 0.0022, 0.0014, 0.002, 0.0022, 0.0028, 0.0026, 0.0018, 0.0017, 0.0013, 0.0013, 0.0013, 0.0014, 0.0022, 0.002, 0.0011, 0.0011, 0.0012, 0.002, 0.0018, 0.0017, 0.0009, 0.0011, 0.002, 0.0018, 0.0015, 0.001, 0.0022, 0.0025, 0.0005, 0.0016, 0.0016, 0.0011, 0.0011, 0.0009, 0.0013, 0.0008, 0.0009, 0.0014, 0.0012, 0.0013, 0.0032, 0.0014, 0.001, 0.001, 0.0011, 0.0011, 0.0019, 0.0016, 0.0016, 0.0011, 0.0008, 0.0008, 0.0031, 0.0021, 0.0012, 0.0018, 0.0013, 0.0009, 0.0011, 0.0019, 0.002, 0.0014, 0.001, 0.0007, 0.003, 0.0014, 0.0013, 0.0012, 0.0013, 0.0012, 0.0009, 0.0008, 0.0012, 0.0014, 0.0011, 0.001, 0.0013, 0.0017, 0.0014, 0.0014, 0.0009, 0.0006, 0.0018, 0.0015, 0.0014, 0.0015, 0.0015, 0.0016, 0.0006, 0.0007, 0.001, 0.0018, 0.0014, 0.0013, 0.0012, 0.0008, 0.0008, 0.001, 0.0016, 0.0016, 0.0007, 0.0015, 0.0012, 0.0011, 0.0015, 0.0013, 0.0012, 0.0012, 0.0008, 0.0008, 0.0011, 0.0011, 0.0072, 0.0011, 0.0011, 0.0017, 0.0014, 0.001, 0.0009, 0.0008, 0.0016, 0.0014, 0.0011, 0.0011, 0.0017, 0.0017, 0.0013, 0.0013, 0.0014, 0.0014, 0.0006, 0.0015, 0.0015, 0.0014, 0.0014, 0.0015] loss_rpn_box_reg: [0.0125, 0.0125, 0.0126, 0.0171, 0.0166, 0.0166, 0.0144, 0.0122, 0.0118, 0.0118, 0.0158, 0.0162, 0.0243, 0.0142, 0.0127, 0.0101, 0.0084, 0.0091, 0.0093, 0.0099, 0.0117, 0.0123, 0.0104, 0.0104, 0.0059, 0.0101, 0.0081, 0.0075, 0.0089, 0.0072, 0.0061, 0.0065, 0.0073, 0.0096, 0.0096, 0.0087, 0.0097, 0.0098, 0.0085, 0.0073, 0.0056, 0.0054, 0.006, 0.0055, 0.0044, 0.0058, 0.0062, 0.0062, 0.0073, 0.003, 0.004, 0.0047, 0.005, 0.0058, 0.0073, 0.0065, 0.0065, 0.0059, 0.0048, 0.0042, 0.0139, 0.0043, 0.0047, 0.0047, 0.0037, 0.0047, 0.0048, 0.0058, 0.0058, 0.0046, 0.005, 0.005, 0.0059, 0.0063, 0.0063, 0.0041, 0.0042, 0.0046, 0.0044, 0.0053, 0.0053, 0.005, 0.005, 0.005, 0.0097, 0.006, 0.0058, 0.0049, 0.0059, 0.0053, 0.0042, 0.0052, 0.0048, 0.0042, 0.0052, 0.005, 0.003, 0.0032, 0.0042, 0.0046, 0.0042, 0.0042, 0.0052, 0.0065, 0.0068, 0.0061, 0.0048, 0.0039, 0.0056, 0.0046, 0.004, 0.004, 0.0043, 0.0052, 0.0054, 0.0043, 0.0053, 0.0055, 0.0054, 0.0053, 0.0083, 0.0055, 0.0047, 0.006, 0.0041, 0.0038, 0.0056, 0.0048, 0.0036, 0.0036, 0.0035, 0.0034, 0.0046, 0.0045, 0.0049, 0.006, 0.0052, 0.0046, 0.0043, 0.004, 0.0053, 0.0053, 0.0058, 0.0065, 0.0036, 0.0043, 0.0047, 0.0047, 0.0047, 0.0041, 0.0057, 0.0057, 0.0042, 0.0044, 0.0056, 0.0056, 0.014, 0.0049, 0.0051, 0.0073, 0.005, 0.005, 0.0039, 0.0031, 0.0032, 0.0035, 0.0049, 0.0049, 0.0047, 0.0062, 0.0049, 0.0049, 0.0059, 0.0051, 0.0037, 0.0042, 0.0052, 0.004, 0.0053, 0.0056] model_time: [] evaluator_time: [] total_time: []
# Example: Extracting the average recall for different thresholds
recall_values_SGD = metrics["AR_100"] # Let's use AR_100 as an example for IoU thresholds plotting
# Example: Precision vs. Recall (assuming AP data correlates with precision directly at different recalls)
precision_values_SGD = metrics["AP"] # Direct extraction for simplicity in this example
import re
# Define dictionaries to hold your data
metrics = {
"AR_1": [],
"AR_10": [],
"AR_100": [],
"AR_small": [],
"AR_medium": [],
"AR_large": [],
"AP": [], # Add AP metric
"AP_50": [], # Add AP_50 metric
"AP_75": [], # Add AP_75 metric
"loss": [], # Add loss metric
"loss_classifier": [], # Add loss_classifier metric
"loss_box_reg": [], # Add loss_box_reg metric
"loss_objectness": [], # Add loss_objectness metric
"loss_rpn_box_reg": [], # Add loss_rpn_box_reg metric
"model_time": [], # Add model_time metric
"evaluator_time": [], # Add evaluator_time metric
"total_time": [] # Add total_time metric
}
# Regex patterns to extract data
pattern_ar_1 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 1 \] = (\d+\.\d+)")
pattern_ar_10 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets= 10 \] = (\d+\.\d+)")
pattern_ar_100 = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_small = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= small \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_medium = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area=medium \| maxDets=100 \] = (\d+\.\d+)")
pattern_ar_large = re.compile(r"Average Recall\s+\(AR\) @\[ IoU=0\.50:0\.95 \| area= large \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50:0\.95 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_50 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.50 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_ap_75 = re.compile(r"Average Precision\s+\(AP\) @\[ IoU=0\.75 \| area= all \| maxDets=100 \] = (\d+\.\d+)")
pattern_loss = re.compile(r"loss: (\d+\.\d+)") # Pattern for loss
pattern_loss_classifier = re.compile(r"loss_classifier: (\d+\.\d+)") # Pattern for loss_classifier
pattern_loss_box_reg = re.compile(r"loss_box_reg: (\d+\.\d+)") # Pattern for loss_box_reg
pattern_loss_objectness = re.compile(r"loss_objectness: (\d+\.\d+)") # Pattern for loss_objectness
pattern_loss_rpn_box_reg = re.compile(r"loss_rpn_box_reg: (\d+\.\d+)")# Pattern for loss_rpn_box_reg
pattern_model_time = re.compile(r"Model time:\s+(\d+\.\d+)") # Pattern for model_time
pattern_evaluator_time = re.compile(r"Evaluator time:\s+(\d+\.\d+)")# Pattern for evaluator_time
pattern_total_time = re.compile(r"Total time:\s+(\d+\.\d+)") # Pattern for total_time
# Read the log file
with open('eva-sgd-adam.txt', 'r') as file:
file_content = file.read()
# Handling AR matches
metrics["AR_1"].extend([float(x) for x in pattern_ar_1.findall(file_content)])
metrics["AR_10"].extend([float(x) for x in pattern_ar_10.findall(file_content)])
metrics["AR_100"].extend([float(x) for x in pattern_ar_100.findall(file_content)])
metrics["AR_small"].extend([float(x) for x in pattern_ar_small.findall(file_content)])
metrics["AR_medium"].extend([float(x) for x in pattern_ar_medium.findall(file_content)])
metrics["AR_large"].extend([float(x) for x in pattern_ar_large.findall(file_content)])
# Handling AP matches
metrics["AP"].extend([float(x) for x in pattern_ap.findall(file_content)])
metrics["AP_50"].extend([float(x) for x in pattern_ap_50.findall(file_content)])
metrics["AP_75"].extend([float(x) for x in pattern_ap_75.findall(file_content)])
# Handling loss matches
metrics["loss"].extend([float(x) for x in pattern_loss.findall(file_content)])
# Handling loss_classifier matches
metrics["loss_classifier"].extend([float(x) for x in pattern_loss_classifier.findall(file_content)])
# Handling loss_box_reg matches
metrics["loss_box_reg"].extend([float(x) for x in pattern_loss_box_reg.findall(file_content)])
# Handling loss_objectness matches
metrics["loss_objectness"].extend([float(x) for x in pattern_loss_objectness.findall(file_content)])
# Handling loss_rpn_box_reg matches
metrics["loss_rpn_box_reg"].extend([float(x) for x in pattern_loss_rpn_box_reg.findall(file_content)])
# Handling model_time matches
metrics["model_time"].extend([float(x) for x in pattern_model_time.findall(file_content)])
# Handling evaluator_time matches
metrics["evaluator_time"].extend([float(x) for x in pattern_evaluator_time.findall(file_content)])
# Handling total_time matches
metrics["total_time"].extend([float(x) for x in pattern_total_time.findall(file_content)])
# Print the collected metrics to verify
for key, value in metrics.items():
print(f"{key}: {value}")
AR_1: [0.155, 0.176, 0.212, 0.201, 0.223, 0.21, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AR_10: [0.361, 0.44, 0.491, 0.492, 0.536, 0.516, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AR_100: [0.417, 0.523, 0.561, 0.573, 0.615, 0.59, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AR_small: [0.397, 0.468, 0.518, 0.549, 0.558, 0.564, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AR_medium: [0.401, 0.529, 0.536, 0.559, 0.612, 0.575, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AR_large: [0.512, 0.525, 0.661, 0.591, 0.678, 0.681, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AP: [0.301, 0.41, 0.462, 0.501, 0.529, 0.515, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] AP_50: [] AP_75: [] loss: [2.4863, 2.3532, 1.1952, 0.9789, 0.9841, 1.1299, 0.9868, 0.8872, 0.7456, 0.6964, 0.6284, 0.6043, 0.4607, 0.4607, 0.5609, 0.4909, 0.47, 0.4464, 0.443, 0.3735, 0.3686, 0.4321, 0.4684, 0.4684, 0.3919, 0.3873, 0.3873, 0.3953, 0.3284, 0.2966, 0.2966, 0.3423, 0.3673, 0.3673, 0.3391, 0.3387, 0.3369, 0.2875, 0.2875, 0.3002, 0.2935, 0.2788, 0.2788, 0.2372, 0.235, 0.2702, 0.2816, 0.3, 0.3058, 0.3005, 0.2973, 0.311, 0.2485, 0.2356, 0.2368, 0.2268, 0.2268, 0.2321, 0.2775, 0.2775, 0.3022, 0.2475, 0.2475, 0.2579, 0.2523, 0.2523, 0.24, 0.2343, 0.2019, 0.2224, 0.2241, 0.2224, 0.2971, 5271547.0, 7212898.5, 1986393.375, 671386.625, 320739.375, 417893.0625, 583216.875, 690144.3125, 311528.8438, 115560.1719, 70980.4688, 6484968.0, 1800933.875, 1776186.625, 617788.625, 2122233.75, 5618370.5, 4074645.75, 51039480.0, 112068888.0, 28259246.0, 28827070.0, 36390144.0, 37082226688.0, 143327376.0, 34987936.0, 22143708.0, 16452572.0, 3729538.75, 3180220.25, 116248224.0, 116248224.0, 47838352.0, 59788872.0, 52835160.0, 52720724.0, 195219552.0, 399970272.0, 395688192.0, 241189632.0, 568194368.0, 271617408.0, 171460368.0, 4976686080.0, 7802124288.0, 61865938944.0, 46775721984.0, 1418080384.0, 31926710272.0, 41220120576.0, 41220120576.0, 102345703424.0, 87810138112.0, 31108685824.0, 43317927936.0, 56634617856.0, 16804518912.0, 10109952000.0, 13764925440.0, 6116950016.0, 205510377472.0, 68524650496.0, 10105732096.0, 18252888064.0, 26050062336.0, 31898882048.0, 245992423424.0, 223513378816.0, 26827456512.0, 80096428032.0, 137168797696.0, 14640758128640.0, 589142556672.0, 205823967232.0, 15113707520.0, 6404826624.0, 6520471552.0, 4512242176.0, 12624415744.0, 29912176640.0, 55517057024.0, 59984121856.0, 60857618432.0, 25029279744.0, 14850866176.0, 12864126976.0, 7330747904.0, 3582105600.0, 2027425024.0, 1145666688.0, 559781504.0, 436397184.0, 368620544.0, 629898304.0, 669884352.0, 543165824.0, 543165824.0, 462647616.0, 253432448.0, 225819984.0, 183020400.0, 185756608.0, 234713504.0, 445998784.0, 5967999488.0, 8547352576.0, 8309713920.0] loss_classifier: [2.2196, 1.7739, 0.7093, 0.4751, 0.472, 0.5273, 0.4433, 0.3999, 0.3241, 0.2928, 0.2484, 0.2494, 0.2299, 0.1716, 0.2009, 0.1881, 0.1476, 0.1311, 0.1287, 0.1132, 0.1228, 0.1619, 0.1619, 0.1619, 0.1312, 0.1226, 0.1133, 0.1046, 0.0813, 0.0813, 0.0899, 0.0982, 0.0986, 0.0901, 0.0993, 0.1035, 0.0912, 0.0764, 0.0786, 0.0796, 0.0807, 0.0864, 0.0765, 0.0662, 0.0586, 0.072, 0.0751, 0.0751, 0.0823, 0.0708, 0.0708, 0.0752, 0.0614, 0.059, 0.0565, 0.0545, 0.0595, 0.0647, 0.0723, 0.0692, 0.0799, 0.0603, 0.0626, 0.0638, 0.0559, 0.0597, 0.0597, 0.0588, 0.0556, 0.0576, 0.0566, 0.0558, 0.087, 324243.6875, 1578566.875, 640492.75, 347828.1875, 67948.3516, 90915.1875, 123241.5078, 176769.7812, 54361.0312, 29951.9961, 15329.9561, 2.3239, 2.4392, 2.4392, 19.9234, 24.8629, 2.4269, 2.4045, 5607205.0, 4182530.25, 250618.8594, 2.4788, 16860.0645, 2.3647, 1758.8846, 2.361, 2.3722, 2.3722, 2.3534, 2.3452, 2.3225, 2.3834, 2.4005, 2.3685, 2.3961, 11789422.0, 2.4235, 2.3975, 2.342, 2.2743, 2.2749, 2.3054, 2.3037, 2.3533, 2.4123, 2.3364, 2.3036, 101655960.0, 3605993.0, 2.4454, 2.313, 2.2816, 2.3102, 2.2372, 2.2508, 2.306, 2.306, 2.2838, 2.3195, 2.3194, 2.3194, 2.2675, 2.2732, 2.3519, 2.22, 2.2632, 2.3389, 2.3131, 2.2631, 2.2955, 2.2692, 523177787392.0, 202637456.0, 2.2525, 2.3488, 2.3488, 2.2346, 2.1972, 2.247, 2.2059, 2.1549, 2.2604, 2.3344, 2.2541, 2.2808, 2.2808, 2.2059, 2.182, 2.2192, 2.2192, 2.1941, 2.1895, 2.2018, 2.2018, 2.1526, 2.4773, 2.1827, 2.2695, 2.3716, 2.3258, 2.325, 2.1143, 2.1625, 2.2757, 2.2456, 2.1364, 2.1364] loss_box_reg: [0.181, 0.3038, 0.3343, 0.3329, 0.3406, 0.4682, 0.4586, 0.4185, 0.3841, 0.3433, 0.3362, 0.3362, 0.2125, 0.254, 0.3167, 0.3065, 0.3019, 0.2988, 0.2808, 0.2591, 0.2189, 0.2561, 0.2748, 0.2769, 0.2455, 0.2455, 0.2537, 0.2453, 0.2054, 0.196, 0.196, 0.2213, 0.2694, 0.2589, 0.2389, 0.2371, 0.2336, 0.1991, 0.1962, 0.2012, 0.2132, 0.195, 0.195, 0.1719, 0.1692, 0.1832, 0.2014, 0.2118, 0.2145, 0.2186, 0.2186, 0.2214, 0.1798, 0.1688, 0.1688, 0.1488, 0.1541, 0.1663, 0.1817, 0.1817, 0.2021, 0.1758, 0.1775, 0.1809, 0.1806, 0.1693, 0.1699, 0.1775, 0.1487, 0.1594, 0.1616, 0.1594, 0.2007, 5142962.5, 5142962.5, 1267061.75, 386138.875, 199476.4375, 320711.4688, 434341.1562, 484874.0, 196912.8281, 55739.7344, 29164.416, 0.0179, 0.7598, 0.7598, 0.1743, 31.9085, 163513.4219, 0.0399, 13121892.0, 5030653.0, 213943.625, 739.4225, 64643.7773, 0.0079, 3567.8777, 0.0114, 0.0062, 0.0094, 0.0036, 0.0021, 0.002, 0.0034, 0.0048, 0.0063, 0.0032, 1178148.5, 0.0045, 0.0034, 0.0025, 0.0016, 0.0012, 0.0018, 0.0018, 0.0013, 0.001, 0.0012, 0.0013, 2866243.75, 558154.4375, 0.0069, 0.003, 0.002, 0.0013, 0.0008, 0.001, 0.001, 0.0016, 0.002, 0.002, 0.001, 0.0011, 0.001, 0.0002, 0.0001, 0.0003, 0.001, 0.0013, 0.0016, 0.0033, 0.0042, 0.0042, 154370490368.0, 0.0045, 0.0013, 0.0013, 0.0012, 0.0007, 0.0004, 0.0006, 0.0007, 0.0007, 0.0015, 0.0015, 0.0005, 0.0005, 0.0006, 0.0017, 0.0025, 0.0015, 0.0025, 0.0028, 0.002, 0.0005, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0, 0.0001, 0.0002, 0.0002, 0.0, 0.0, 0.0008, 0.002, 0.002] loss_objectness: [0.0736, 0.2685, 0.109, 0.0847, 0.0753, 0.0493, 0.0371, 0.0316, 0.0186, 0.0179, 0.0201, 0.02, 0.014, 0.0145, 0.0179, 0.0124, 0.0086, 0.0099, 0.0087, 0.0067, 0.0098, 0.0115, 0.0112, 0.0117, 0.0062, 0.007, 0.0067, 0.0055, 0.0054, 0.0039, 0.0047, 0.005, 0.0055, 0.0033, 0.0039, 0.005, 0.0027, 0.0026, 0.003, 0.0022, 0.0019, 0.0023, 0.0027, 0.0017, 0.0014, 0.0014, 0.0026, 0.0033, 0.004, 0.0016, 0.0017, 0.0015, 0.0012, 0.0008, 0.0008, 0.0012, 0.0017, 0.0015, 0.0015, 0.0015, 0.0024, 0.0023, 0.0023, 0.0017, 0.0015, 0.0012, 0.0014, 0.001, 0.0006, 0.0011, 0.0013, 0.0014, 0.0012, 40562.7852, 52229.1914, 48171.1367, 20998.1211, 6489.3223, 9490.4365, 8365.3896, 7892.6016, 6596.0479, 4572.4634, 5286.834, 4961285.5, 677858.625, 555855.0, 292421.375, 509012.5, 3614144.0, 1587095.875, 3438756.0, 10105214.0, 8628231.0, 8628231.0, 12218656.0, 20404789248.0, 76122056.0, 17763510.0, 11812624.0, 3748131.75, 1942666.0, 824644.1875, 64077120.0, 64077120.0, 11815075.0, 12146524.0, 6192073.0, 13265775.0, 27572750.0, 54201908.0, 72947248.0, 72947248.0, 100719768.0, 152921584.0, 152921584.0, 3005594624.0, 3005594624.0, 15097151488.0, 15097151488.0, 370253568.0, 12318129152.0, 12318129152.0, 15383909376.0, 49936527360.0, 23787079680.0, 7051922944.0, 9142196224.0, 9748790272.0, 3319812864.0, 2783002624.0, 3333287680.0, 1974285952.0, 61073563648.0, 23916335104.0, 3902347008.0, 5904737792.0, 5904737792.0, 7679072768.0, 104155185152.0, 67303845888.0, 9242958848.0, 11397070848.0, 22049380352.0, 1826739322880.0, 159639027712.0, 31965216768.0, 5249129984.0, 1891344640.0, 1573129216.0, 1314357120.0, 4008590080.0, 8773196800.0, 20124471296.0, 16760728576.0, 16760728576.0, 9958882304.0, 5928898560.0, 4343258624.0, 1916079360.0, 939273664.0, 629265088.0, 481244416.0, 205151792.0, 137910896.0, 110538704.0, 168628448.0, 222643696.0, 253653280.0, 222960896.0, 139691040.0, 121091456.0, 72755192.0, 58658920.0, 56773184.0, 91250544.0, 117835960.0, 1534850048.0, 2316171264.0, 2258605824.0] loss_rpn_box_reg: [0.0122, 0.023, 0.0206, 0.0176, 0.0175, 0.017, 0.0152, 0.0145, 0.0146, 0.015, 0.0117, 0.0117, 0.0042, 0.0093, 0.0107, 0.0102, 0.0086, 0.0113, 0.0099, 0.0085, 0.0086, 0.0102, 0.0093, 0.0096, 0.009, 0.0111, 0.0111, 0.0107, 0.0071, 0.007, 0.0071, 0.0076, 0.0084, 0.0084, 0.0075, 0.0075, 0.0094, 0.0058, 0.0069, 0.008, 0.006, 0.0069, 0.007, 0.0078, 0.0068, 0.0064, 0.0061, 0.0061, 0.005, 0.0061, 0.0066, 0.0077, 0.0059, 0.0045, 0.0049, 0.0048, 0.0044, 0.0044, 0.0058, 0.0066, 0.0178, 0.0063, 0.0059, 0.0058, 0.005, 0.0053, 0.0056, 0.0042, 0.0037, 0.0052, 0.0054, 0.0047, 0.0083, 4122.6558, 16300.8662, 17248.6602, 7573.0581, 4729.5298, 7850.1875, 24332.0605, 24332.0605, 6414.6733, 6141.377, 11858.5957, 1523679.875, 57047.1758, 88967.75, 113751.0234, 325364.8438, 1778727.375, 1167142.625, 3055371.75, 11142872.0, 12528507.0, 15887939.0, 21355252.0, 16677439488.0, 51280272.0, 12814747.0, 6924202.5, 5021109.5, 1700890.25, 862957.8125, 41252116.0, 41425260.0, 22989032.0, 47973796.0, 43746984.0, 26487380.0, 174136864.0, 352447072.0, 194540448.0, 120696296.0, 226160800.0, 63684192.0, 74702128.0, 1971091328.0, 5083697152.0, 44886159360.0, 34794504192.0, 943304640.0, 15580342272.0, 25458366464.0, 25458366464.0, 64111980544.0, 64023056384.0, 22132686848.0, 29597784064.0, 29597784064.0, 10248808448.0, 6831554560.0, 7227887616.0, 4142664192.0, 144436805632.0, 44608315392.0, 6446649344.0, 11398449152.0, 19815819264.0, 20694464512.0, 119358201856.0, 98220417024.0, 17584498688.0, 68699357184.0, 108964569088.0, 12136470282240.0, 510677483520.0, 174832304128.0, 13222363136.0, 4024046336.0, 4468099584.0, 2940316416.0, 8586808832.0, 20942501888.0, 46743859200.0, 43685576704.0, 52968079360.0, 15070398464.0, 8562162176.0, 8059048960.0, 4783551488.0, 2039989632.0, 1187719808.0, 456337568.0, 341187520.0, 286260928.0, 216146336.0, 462010880.0, 497745760.0, 289512544.0, 375960640.0, 297198336.0, 137349200.0, 146000160.0, 122740864.0, 125930776.0, 149373344.0, 215782768.0, 3676463616.0, 6298272256.0, 5892077056.0] model_time: [] evaluator_time: [] total_time: []
# Example: Extracting the average recall for different thresholds
recall_values_SGDADAMN = metrics["AR_100"] # Let's use AR_100 as an example for IoU thresholds plotting
# Example: Precision vs. Recall (assuming AP data correlates with precision directly at different recalls)
precision_values_SGDADAMN = metrics["AP"] # Direct extraction for simplicity in this example
import matplotlib.pyplot as plt
# Sort the data by recall since the precision-recall curve expects this.
sorted_indices_ADAMNSGD = sorted(range(len(recall_values_ADAMNSGD)), key=lambda k: recall_values_ADAMNSGD[k])
precision_values_ADAMNSGD_sorted = [precision_values_ADAMNSGD[i] for i in sorted_indices_ADAMNSGD]
recall_values_ADAMNSGD_sorted = [recall_values_ADAMNSGD[i] for i in sorted_indices_ADAMNSGD]
# To ensure the plot fully spans, check starts and ends
if recall_values_ADAMNSGD_sorted[0] > 0:
recall_values_ADAMNSGD_sorted.insert(0, 0)
precision_values_ADAMNSGD_sorted.insert(0, precision_values_ADAMNSGD[0])
if recall_values_ADAMNSGD_sorted[-1] < 1:
recall_values_ADAMNSGD_sorted.append(1)
precision_values_ADAMNSGD_sorted.append(precision_values_ADAMNSGD[-1])
# Sort the data for SGD since the precision-recall curve expects this.
sorted_indices_SGD = sorted(range(len(recall_values_SGD)), key=lambda k: recall_values_SGD[k])
precision_values_SGD_sorted = [precision_values_SGD[i] for i in sorted_indices_SGD]
recall_values_SGD_sorted = [recall_values_SGD[i] for i in sorted_indices_SGD]
# To ensure the plot fully spans, check starts and ends
if recall_values_SGD_sorted[0] > 0:
recall_values_SGD_sorted.insert(0, 0)
precision_values_SGD_sorted.insert(0, precision_values_SGD[0])
if recall_values_SGD_sorted[-1] < 1:
recall_values_SGD_sorted.append(1)
precision_values_SGD_sorted.append(precision_values_SGD[-1])
# Sort the data for SGDADAMN since the precision-recall curve expects this.
sorted_indices_SGDADAMN = sorted(range(len(recall_values_SGDADAMN)), key=lambda k: recall_values_SGDADAMN[k])
precision_values_SGDADAMN_sorted = [precision_values_SGDADAMN[i] for i in sorted_indices_SGDADAMN]
recall_values_SGDADAMN_sorted = [recall_values_SGDADAMN[i] for i in sorted_indices_SGDADAMN]
# To ensure the plot fully spans, check starts and ends
if recall_values_SGDADAMN_sorted[0] > 0:
recall_values_SGDADAMN_sorted.insert(0, 0)
precision_values_SGDADAMN_sorted.insert(0, precision_values_SGDADAMN[0])
if recall_values_SGDADAMN_sorted[-1] < 1:
recall_values_SGDADAMN_sorted.append(1)
precision_values_SGDADAMN_sorted.append(precision_values_SGDADAMN[-1])
# Sort the data for Adam since the precision-recall curve expects this.
sorted_indices_Adam = sorted(range(len(recall_values_Adam)), key=lambda k: recall_values_Adam[k])
precision_values_Adam_sorted = [precision_values_Adam[i] for i in sorted_indices_Adam]
recall_values_Adam_sorted = [recall_values_Adam[i] for i in sorted_indices_Adam]
# To ensure the plot fully spans, check starts and ends
if recall_values_Adam_sorted[0] > 0:
recall_values_Adam_sorted.insert(0, 0)
precision_values_Adam_sorted.insert(0, precision_values_Adam[0])
if recall_values_Adam_sorted[-1] < 1:
recall_values_Adam_sorted.append(1)
precision_values_Adam_sorted.append(precision_values_Adam[-1])
# Sort the data for RMSprop since the precision-recall curve expects this.
sorted_indices_RMSprop = sorted(range(len(recall_values_RMSprop)), key=lambda k: recall_values_RMSprop[k])
precision_values_RMSprop_sorted = [precision_values_RMSprop[i] for i in sorted_indices_RMSprop]
recall_values_RMSprop_sorted = [recall_values_RMSprop[i] for i in sorted_indices_RMSprop]
# To ensure the plot fully spans, check starts and ends
if recall_values_RMSprop_sorted[0] > 0:
recall_values_RMSprop_sorted.insert(0, 0)
precision_values_RMSprop_sorted.insert(0, precision_values_RMSprop[0])
if recall_values_RMSprop_sorted[-1] < 1:
recall_values_RMSprop_sorted.append(1)
precision_values_RMSprop_sorted.append(precision_values_RMSprop[-1])
# Sort the data for Adelta since the precision-recall curve expects this.
sorted_indices_Adelta = sorted(range(len(recall_values_Adelta)), key=lambda k: recall_values_Adelta[k])
precision_values_Adelta_sorted = [precision_values_Adelta[i] for i in sorted_indices_Adelta]
recall_values_Adelta_sorted = [recall_values_Adelta[i] for i in sorted_indices_Adelta]
# To ensure the plot fully spans, check starts and ends
if recall_values_Adelta_sorted[0] > 0:
recall_values_Adelta_sorted.insert(0, 0)
precision_values_Adelta_sorted.insert(0, precision_values_Adelta[0])
if recall_values_Adelta_sorted[-1] < 1:
recall_values_Adelta_sorted.append(1)
precision_values_Adelta_sorted.append(precision_values_Adelta[-1])
# Create the step plot for the precision-recall curve
plt.figure(figsize=(10, 5))
# Plot for AdamSGD
plt.step(recall_values_ADAMNSGD_sorted, precision_values_ADAMNSGD_sorted, where='post', color='purple', linewidth=2.5, label='Precision vs. Recall (AdamSGD)')
# Plot for SGD
plt.step(recall_values_SGD_sorted, precision_values_SGD_sorted, where='post', color='blue', linewidth=2.5, label='Precision vs. Recall (SGD)')
# Plot for SGDADAMN
plt.step(recall_values_SGDADAMN_sorted, precision_values_SGDADAMN_sorted, where='post', color='green', linewidth=2.5, label='Precision vs. Recall (SGDADAMN)')
# Plot for Adam
plt.step(recall_values_Adam_sorted, precision_values_Adam_sorted, where='post', color='orange', linewidth=2.5, label='Precision vs. Recall (Adam)')
# Plot for RMSprop
plt.step(recall_values_RMSprop_sorted, precision_values_RMSprop_sorted, where='post', color='red', linewidth=2.5, label='Precision vs. Recall (RMSprop)')
# Plot for Adelta
plt.step(recall_values_Adelta_sorted, precision_values_Adelta_sorted, where='post', color='cyan', linewidth=2.5, label='Precision vs. Recall (Adelta)')
# Customize the plot
plt.xlabel('Recall')
plt.ylabel('Precision')
# Title for AdamSGD
plt.text(0.15, 1.05, ' --AdamSGD', color='purple', fontsize=12, ha='left', va='bottom', transform=plt.gca().transAxes)
# Title for SGD
plt.text(0.30, 1.05, ' --SGD', color='blue', fontsize=12, ha='left', va='bottom', transform=plt.gca().transAxes)
# Title for SGDADAMN
plt.text(0.38, 1.05, ' --SGDADAMN', color='green', fontsize=12, ha='left', va='bottom', transform=plt.gca().transAxes)
# Title for Adam
plt.text(0.55, 1.05, ' --Adam', color='orange', fontsize=12, ha='left', va='bottom', transform=plt.gca().transAxes)
# Title for RMSprop
plt.text(0.65, 1.05, ' --RMSprop', color='red', fontsize=12, ha='left', va='bottom', transform=plt.gca().transAxes)
# Title for Adelta
plt.text(0.79, 1.05, ' --Adelta', color='cyan', fontsize=12, ha='left', va='bottom', transform=plt.gca().transAxes)
plt.xlim(0, 1)
plt.ylim(0, 1.2)
plt.grid(True)
# Draw a horizontal line at y=1
plt.axhline(y=1, color='red', linestyle='-', linewidth=3.5, label='Max Precision')
# Enhance the legend to include stylistic references
plt.legend(title='Legend', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
import matplotlib.pyplot as plt
# Define the IoU thresholds from 0 to 1 with an increment of 0.2
iou_thresholds = [i / 5 for i in range(6)]
# Your provided recall values. Assuming each array has more than 6 elements, slice to the first 6.
recall_values_ADAMNSGD = recall_values_ADAMNSGD[:6] # Slice to the first 6 values
recall_values_SGD = recall_values_SGD[:6] # Slice to the first 6 values
recall_values_SGDADAMN = recall_values_SGDADAMN[:6] # Slice to the first 6 values
recall_values_Adam = recall_values_Adam[:6] # Slice to the first 6 values
recall_values_RMSprop = recall_values_RMSprop[:6] # Slice to the first 6 values
recall_values_Adelta = recall_values_Adelta[:6] # Slice to the first 6 values
plt.figure(figsize=(10, 5))
# Plot each optimizer with points marked by 'o'
plt.plot(iou_thresholds, recall_values_ADAMNSGD, marker='o', linestyle='-', color='cyan', label='AdamSGD')
plt.plot(iou_thresholds, recall_values_SGD, marker='o', linestyle='-', color='purple', label='SGD')
plt.plot(iou_thresholds, recall_values_SGDADAMN, marker='o', linestyle='-', color='blue', label='SGDADAMN')
plt.plot(iou_thresholds, recall_values_Adam, marker='o', linestyle='-', color='green', label='Adam')
plt.plot(iou_thresholds, recall_values_RMSprop, marker='o', linestyle='-', color='orange', label='RMSprop')
plt.plot(iou_thresholds, recall_values_Adelta, marker='o', linestyle='-', color='red', label='Adelta')
# Title for AdamSGD
plt.text(0.15, 1.05, ' --AdamSGD', color='cyan', fontsize=12, ha='left', va='bottom', transform=plt.gca().transAxes)
# Title for SGD
plt.text(0.30, 1.05, ' --SGD', color='purple', fontsize=12, ha='left', va='bottom', transform=plt.gca().transAxes)
# Title for SGDADAMN
plt.text(0.38, 1.05, ' --SGDADAMN', color='blue', fontsize=12, ha='left', va='bottom', transform=plt.gca().transAxes)
# Title for Adam
plt.text(0.55, 1.05, ' --Adam', color='green', fontsize=12, ha='left', va='bottom', transform=plt.gca().transAxes)
# Title for RMSprop
plt.text(0.65, 1.05, ' --RMSprop', color='orange', fontsize=12, ha='left', va='bottom', transform=plt.gca().transAxes)
# Title for Adelta
plt.text(0.79, 1.05, ' --Adelta', color='red', fontsize=12, ha='left', va='bottom', transform=plt.gca().transAxes)
# Customize the plot
plt.xlabel('IoU Threshold')
plt.ylabel('Recall')
plt.xticks(iou_thresholds)
plt.xlim(0, 1)
plt.ylim(min(min(recall_values_ADAMNSGD, recall_values_SGD, recall_values_SGDADAMN, recall_values_Adam, recall_values_RMSprop, recall_values_Adelta)) - 0.05,
max(max(recall_values_ADAMNSGD, recall_values_SGD, recall_values_SGDADAMN, recall_values_Adam, recall_values_RMSprop, recall_values_Adelta)) + 0.05)
plt.grid(True)
plt.legend(title='Optimizer', loc='upper right')
plt.show()
import pickle
# Define the file path where you want to save the model
Filename = "/content/drive/MyDrive/dataset/FRCNN2adamn.pkl"
# Save the Modle to file in the current working directory
with open(Filename, 'wb') as file:
pickle.dump(model, file)
# Load the Model back from file
with open(Filename, 'rb') as file:
model = pickle.load(file)
model
FasterRCNN(
(transform): GeneralizedRCNNTransform(
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
Resize(min_size=(800,), max_size=1333, mode='bilinear')
)
(backbone): BackboneWithFPN(
(body): IntermediateLayerGetter(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): FrozenBatchNorm2d(256, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(512, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(1024, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(2048, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
)
)
(fpn): FeaturePyramidNetwork(
(inner_blocks): ModuleList(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
)
(1): Conv2dNormActivation(
(0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
(2): Conv2dNormActivation(
(0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
)
(3): Conv2dNormActivation(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
)
)
(layer_blocks): ModuleList(
(0-3): 4 x Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(extra_blocks): LastLevelMaxPool()
)
)
(rpn): RegionProposalNetwork(
(anchor_generator): AnchorGenerator()
(head): RPNHead(
(conv): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
)
)
(cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
(bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
)
)
(roi_heads): RoIHeads(
(box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)
(box_head): TwoMLPHead(
(fc6): Linear(in_features=12544, out_features=1024, bias=True)
(fc7): Linear(in_features=1024, out_features=1024, bias=True)
)
(box_predictor): FastRCNNPredictor(
(cls_score): Linear(in_features=1024, out_features=11, bias=True)
(bbox_pred): Linear(in_features=1024, out_features=44, bias=True)
)
)
)
# function to convert a torchtensor back to PIL image
def torch_to_pil(img):
return torchtrans.ToPILImage()(img).convert('RGB')
# pick one image from the test set
img, target = dataset_test[30]
# put the model in evaluation mode
model.eval()
with torch.no_grad():
prediction = model([img.to(device)])[0]
print('predicted #boxes: ', prediction['labels'])
print('real #boxes: ', target['labels'])
predicted #boxes: tensor([10, 10, 10, 10, 10, 10, 10, 1, 10], device='cuda:0') real #boxes: tensor([10, 10, 10, 10, 10, 10])
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
def plot_img_bbox(img, target):
# Convert PIL Image to NumPy array
img_array = np.array(img)
# Permute dimensions if it's a PyTorch tensor
if isinstance(img_array, torch.Tensor):
img_array = img_array.permute(1, 2, 0)
# Plot the image and bounding boxes
fig, a = plt.subplots(1,1)
fig.set_size_inches(5,5)
a.imshow(img_array)
for box in target['boxes']:
x, y, w, h = box
rect = plt.Rectangle((x, y), w, h, fill=False, edgecolor='red', linewidth=2)
a.add_patch(rect)
plt.show()
print('EXPECTED OUTPUT')
plot_img_bbox(torch_to_pil(img), target)
EXPECTED OUTPUT
import torch
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
def plot_img_bbox(img, target):
fig, a = plt.subplots(1, 1)
fig.set_size_inches(5, 5)
a.imshow(img)
# print(target['boxes'])
for box in target['boxes']:
x, y, w, h = box.cpu().numpy() # Move the tensor to CPU and convert to NumPy array
#print("-------x------------")
#print(x)
#print("-------y------------")
#print(y)
#print("-------w------------")
#print(w)
#print("-------h------------")
#print(h)
width, height = w-x, h-y
rect = patches.Rectangle((x, y), width, height, linewidth=2, edgecolor='r', facecolor='none')
a.add_patch(rect)
plt.show()
print('MODEL OUTPUT')
plot_img_bbox(torch_to_pil(img), prediction)
MODEL OUTPUT
# the function takes the original prediction and the iou threshold.
def apply_nms(orig_prediction, iou_thresh=0.3):
# torchvision returns the indices of the bboxes to keep
keep = torchvision.ops.nms(orig_prediction['boxes'], orig_prediction['scores'], iou_thresh)
final_prediction = orig_prediction
final_prediction['boxes'] = final_prediction['boxes'][keep]
final_prediction['scores'] = final_prediction['scores'][keep]
final_prediction['labels'] = final_prediction['labels'][keep]
return final_prediction
nms_prediction = apply_nms(prediction, iou_thresh=0.2)
print('NMS APPLIED MODEL OUTPUT')
plot_img_bbox(torch_to_pil(img), nms_prediction)
NMS APPLIED MODEL OUTPUT